March 8, 2024 PAO-03-24-CL-02
One strategy we see employed is to ensure that supply of needed materials, equipment, chemicals, etc. is on hand. The pandemic, and ensuing after effects, reminded manufacturers that supply chain security is not a given. The strategy of establishing inventory where appropriate and feasible can greatly help if delivery of validated materials needed for drug manufacturing are slowed or temporarily halted. While this of course applies to products currently being manufactured, it is no less vital in the development and commercialization stages. Increasing the time to market due to equipment and materials shortages – be they detergents, filters, disposable items, etc. – that are a fraction of the overall cost of the manufacturing process, is an easily avoidable error. Inventory, or personal stocking agreements with your vendor, can help greatly reduce this issue without any compromise to quality.
Another helpful strategy is to closely partner with your vendor in the product and process development stages. Many vendors of critical equipment have a wealth of experience and knowledge that can be applied to product development, validation, process controls, and other critical parameters.
Some key areas of focus include automation across manufacturing workflows to increase efficiencies and reduce costs, computational/AI platforms that enable predictive analytics to provide earlier visibility into a drug’s potential safety/efficacy profile or more quickly test targets or molecules in order “fail faster” and avoid costly preclinical/clinical development work.
For automation, we see everything from instrument level automation (i.e. push-button operation) to robotics-based platforms that aim to take the manual component out of an entire manufacturing process.
Computational and/or AI platforms have mostly been focused on earlier stage drug discovery, but there’s an opportunity across the entire spectrum of the drug development cycle to more effectively capture, organize, structure and analyze data, including within manufacturing processes.
The way to ensure quality is not compromised with new approaches is to generate robust data showing comparability, or even better, superiority to existing processes to get various stakeholders comfortable (drug developers, manufacturers, regulators, payers, providers, etc.). Then, early adopters will be needed to show commercial feasibility and establish precedent.
Growing demand for innovative therapies, increasing market competition, the cost of drug development, and limited patent life are putting pressure on the biopharmaceutical industry to accelerate product development. Companies that compromise on quality risk compromising public health, increased regulatory hurdles, serious reputational damage, and loss of market share.
Strategies to reduce time-to-market while prioritizing quality include selecting the right bioprocess ingredients manufacturer; developing standardized, robust, high-performance, and scalable technology platforms; and using process intensification and simplification techniques, such as N-1 perfusion or concentrated fed-batch processes.
Requirements for cell culture media and ingredients have evolved, including addressing the inherent solubility and stability limitations of amino acids that add a lot of variability into bioprocessing. As an example, we as Evonik provide chemically defined peptides as part of our cQrex® portfolio that help media and process developers to formulate highly concentrated and stable media, reducing costs and ensuring robustness. These peptides have become an integral part of technology platforms and are used in many pipeline and commercial programs and can help to accelerate development and commercialization timelines.
In general, the focus on raw material quality and supply security has further increased in recent years, as it can impact time-to-market and ensure business continuity. Impurities in chemically defined materials can have a major impact on product quality, so well defined and strict specifications and robust change control procedures must be in place to avoid deviations. Sourcing from a reliable manufacturer with sufficient capacity can ensure this. Again, as an example, Evonik cell culture ingredients are produced at several sites in our manufacturing network and meet the highest quality requirements in the industry.
The biopharmaceutical industry has been actively exploring various strategies to accelerate product development and commercialization while maintaining high-quality standards. Some of the key strategies include:
1. Advanced rechnologies:
• Using artificial intelligence (AI) and machine learning (ML) for drug discovery, target identification, and predictive analytics to streamline the research and development process.
• Developing platform technologies that can be applied to multiple products or therapeutic areas, allowing for more efficient development processes and economies of scale.
• Analyzing large data sets to gain insights into disease mechanisms, patient populations, and clinical trial outcomes, enabling more informed decision-making.
• Employing adaptive trial designs that allow for real-time adjustments based on interim data analysis. This helps optimize the trial process and reduce timelines.
2. Continuous manufacturing:
• Transitioning from traditional batch manufacturing to continuous manufacturing processes, which can lead to increased efficiency, reduced costs, and a more agile response to market demands.
3. Collaborative Partnerships and Regulatory Pathways:
• Forming strategic collaborations and partnerships with academic institutions and other pharmaceutical companies to leverage complementary expertise, resources, and infrastructure.
• Taking advantage of expedited regulatory pathways, such as the FDA's Fast Track, Breakthrough Therapy, and Accelerated Approval programs.
By combining these strategies, the biopharmaceutical industry can combat challenges associated with time-to-market pressures, accelerate innovation, and bring safe and effective therapies to patients more rapidly.
Sensor technologies can be use as part of a process analytical tools (PAT) initiative to measure specific critical process parameters (CPPs) and key process indicators (KPIs) previously determined as important variables affecting the presence of critical quality attributes (CQAs) in the final product. Proper application of PAT during the earlier stages will minimize scale-up/scale-down iterations, which often cause delays. When implemented in-line, process sensors not only monitor processes in real time but are also gateway devices toward automation and reducing time-to-market while simultaneously maintaining product quality.
At Hamilton Process Analytics we offer the tools for what we call "Intelligent PAT," defined by two core principles: 1) monitoring multiple CPPs in-line and 2) seamless digitalization. Tools that strongly adhere to these two principles — including Hamilton’s “Intelligent" Arc sensors — can be easily integrated at every level of the IT/OT infrastructure to deliver information at every step. The Arc sensors integrate a microtransmitter, which compensates and digitalizes the signal (avoiding interference) and provides real-time indications of sensor health status. In short, Intelligent Arc sensors don’t just offer easy integration with PLC or SCADA systems but also deliver the most accurate real-time measurement of processes while simultaneously indicating measurement reliability. This Information is essential to automatically regulate bioprocesses and ensure products compare to the ideal "golden batch."
Drug discovery is a complex process with many bottlenecks that can hinder the success of the target molecule. Several major challenges can be identified for the development stage, such as predicting the pharmacokinetics, solubility, and bioavailability of the final active pharmaceutical ingredient (API).
Limitations can occur at the earliest stages of drug discovery, too. With the increasing complexity of small molecule APIs entering the drug development pipeline, the synthetic routes are becoming longer and more challenging for achieving sufficient yields and purity.
Selecting the best synthetic route represents an ideal strategy to de-risk and optimize the overall time-to-market, environmental impact, by-product formation, purification, or other quality- and speed-impacting parameters. Route scouting is, therefore, a great solution that helps drug developers identify optimal synthetic routes quickly and efficiently. Lonza utilizes AI and machine learning (ML) algorithms alongside the experience and expertise of synthetic chemists and supply chain experts to accelerate the synthesis of APIs from hit-selection up to commercial-scale manufacturing.
The biopharmaceutical industry is finding ways to speed up product development and getting to market quicker by using AI and automation to analyze data faster and make processes more efficient. They're also being more flexible in decision-making and team collaboration. Plus, there's a lot of sharing going on between different groups to help move along smoothly. Regulatory agencies are working toward reducing time and offering pathways for faster approval.
Olon Group is significantly expanding its generic pipeline, which is already one of the broadest and most diversified in the market thanks to the Olon Group's strategy of constantly investing in research applied to the development of new molecule synthesis routes, with a particular focus on active ingredients of innovative therapies with high scientific value, which will be available on the generic market in the long term.
Developing and implementing templates can greatly speed up product development. Using established process steps and analytical platforms, the development timeline can be reduced by three to six months. These templates are like a toolbox that contains validated process steps that have been tested with various vectors over the years. This means that the quality of the final product can be easily predicted. For example, using a standardized backbone for lentiviruses not only results in consistent productivity but also improves quality, as shown by some process developers. Knowing the average productivity and quality achieved through this method means that the process development can be simplified to pilot manufacturing. There is no need to develop an analytical platform to match the backbone features, and the pilot material can be used for further downstream purification optimizations, if necessary. The next step is to directly move toward engineering batch manufacturing or clinical GMP. The backbone is just one tool in the kit, along with the cell lines, culture media, productivity enhancers, and downstream steps, all of which are standardized and routinely implemented. CDMOs develop tens of programs per year, so it doesn't take long to validate these platforms and provide users with templates that have been successfully used multiple times.
We continue to see innovative digital and technology-driven study approaches gaining traction to increase patient centricity, lower site burden, drive efficiencies, and improve diversity in patient enrollment. We are expanding solutions to meet sponsors’ needs by incorporating such innovations as virtual and mobile site services, decentralized and digital trials, virtual enrollment and prescreening, remote monitoring, eDiaries and wearables, RWE, and AI. The latter is of particular interest in clinical research, and generative AI offers the potential to revolutionize the way we deliver value to our customers and colleagues. For example, predictive modeling infused with AI and RWE gives us the ability to enhance study design, guide country/site selection, accelerate site activation, speed recruitment timelines, identify and mitigate risks, and automate standard tasks for optimal speed and efficacy. The responsible use of this developing technology will help ensure we stay at the leading edge of serving science.
It would be fair to say that all players in the biopharmaceutical industry work to achieve one goal: to produce and deliver high-quality drugs to support patients facing life-threatening diseases and their family members. With the increased demand for biologics due to a higher prevalence of advanced diseases, one-team collaboration within the biopharmaceutical industry is critical to expediting product development and, in turn, saving lives.
On that note, being first to market is a huge industry factor in the pharmaceutical landscape. Entrusting development and manufacturing to an experienced CDMO can accelerate product development and launch. CDMOs like Samsung Biologics have the capacity, scale, and necessary facilities and equipment to meet demand and changes in production volume.
Working and aligning with regulatory bodies early in the clinical study design phase is also imperative for drug manufacturers to ensure the review process is faster and more efficient without the risk of delay, allowing our clients to solely focus on executing their projects without facing any hurdles.
Digitalization strategies during manufacturing can further drive efficiency and rapid commercialization. Quality systems with appropriate digitalization and automation allow for a consistent and reliable process time over time. To minimize any chance of human errors in data recording, we have serialized the manufacturing process, which allows our clients to monitor and track their project status in real time. Packaging and labeling of a drug product can also be carried out with effective automation.
Accelerating the development of a candidate drug from discovery to commercialization by even a single year can bring breakthrough therapies to individuals with limited treatment options.
Establishing robust and scalable early-stage formulations and processes can circumvent the need for relative bioavailability (RBA) studies by eliminating the necessity to overhaul the formulation and process as the candidate progresses into later clinical trials. This can significantly reduce development timelines, often by as much as three to four years, and shift the focus from reformulation to process development and scalability.
As clinical development progresses, process optimization and scale-up is expected, but with the formulation design space and unit operations for manufacturing defined early, these activities can build toward a well-defined operating space with demonstrated performance in a cGMP environment while ensuring scalability and flexibility.
All these pre-registration activities are being executed using the product life cycle approach as part of Stage 1 process validation (PV) as stipulated by global regulatory agencies, maximizing the utilization of stability and analytical method data across all phases of clinical development.
Failing to consider advanceable formulations and processes during the early development phases not only jeopardizes a product's potential value but also diminishes its marketability.
The biopharmaceutical industry is actively pursuing several strategies to expedite product development and commercialization while maintaining high standards of quality. With a growing emphasis on efficiency, companies are turning to advanced technologies such as artificial intelligence and machine learning to streamline the drug discovery and development processes. Moreover, there is a pronounced shift towards collaborative efforts and partnerships within the industry, facilitating the sharing of resources, knowledge, and expertise to expedite research and development timelines. Additionally, the industry is leveraging real-world evidence and patient data to inform clinical trials and regulatory processes, enabling faster decision-making. Embracing agile methodologies has also become common practice, allowing for increased flexibility in project management and quicker adaptation to changing requirements and insights. Furthermore, the adoption of continuous manufacturing processes is gaining traction, effectively reducing production timelines and enhancing overall efficiency. The application of technology in manufacturing including pharmaceutical analytical technology, machine learning and real time release are effectively increasing efficiency and reducing costs. These strategies collectively reflect the industry's commitment to meeting the demand for accelerated product development and commercialization without compromising on the quality of pharmaceutical products.
The pharmaceutical industry faces a dual challenge: developing impactful medicines swiftly while maintaining quality. A key driver for this is that the industry adopts and implement better and more comprehensive process analytical tools (PAT), such as liquid chromatography (LC) and mass spectrometry (MS), in their processes. As a technology provider, Waters can contribute by developing and offering PAT alternatives that in a simple manner provide the required data faster and better without compromising on quality. The BioAccord LC-MS System has been designed with this in mind, providing automated workflows and solutions to enable bioprocessors to obtain critical data when and where they need it.
In addition to LC-MS, there are other PAT options to monitor and control bioprocesses. For instance, Raman spectroscopy, in-line capacitance, and multi angle light scattering (MALS) are some of the technologies that are currently in use. These provide real-time process monitoring capabilities for connected and continuous bioprocessing, enabling real time drug manufacturing and release. Another approach is to use digital enablers, such as AI, ML, and process automation to create more agile research and development processes. And, of course, the synergy between advanced PAT tools and digital enablers holds immense potential. By embracing these innovations, we can usher in an era of accelerated drug development and manufacturing, benefiting patients worldwide.
The COVID-19 pandemic highlighted the need for efficiency and speed to rapidly bring new treatments to society to tackle the rapidly spreading disease. While mRNA drugs were the first to be approved, the urgent need for parallel solutions also called for innovation of the traditional antibody clinical path.
Historically, antibody therapeutics proceeded through the time-consuming clone selection stage, from which material was generated for clinical testing. However, recent advances in the technologies used for introducing the gene-of-interest into a production cell allowed for improvements in the traditional therapeutic antibody workflow. One such technology that was pressure tested and excelled during the pandemic was the use of transposonal elements, made famous by Dr. Barbara McClintock’s “jumping genes,’ for which she was awarded the Nobel in Physiology and Medicine in 1983.
Transposon technology allows for the rapid generation of stable bulk pools, which in turn facilitates the accelerated manufacture of representative clinical material and therapeutic validation. This approach eliminates months of the parallel clonal cell line workflow, which is also undertaken to support later commercial manufacture.
Traditionally, the journey from product conception to market release could span a decade. However, the biopharmaceutical industry’s increased use of AI and machine learning ML technologies holds the promise of substantially reducing development timelines while upholding quality standards, as these technologies facilitate real-time oversight and control of crucial process variables. In light of the expanding development of advanced therapies, such as cell and gene therapies that are characterized by limited sample sizes and a need for faster processes, the incorporation of these technologies becomes even more essential.
Through the utilization of AI/ML, biopharmaceutical scientists can introduce more data-driven and efficient operations. This involves incorporating dynamic process controls and promptly identifying process anomalies in real time to prevent operational failures, thereby ensuring the safety and efficacy of the product. These technologies also streamline manual tasks like bioreactor sampling, saving operator time, accelerating workflows, and minimizing the risk of contamination. Furthermore, the incorporation of real-time monitoring technologies reduces batch-to-batch variability, facilitating faster regulatory compliance by furnishing evidence that the process adheres to defined limits for efficacy and safety.
By transitioning from periodic offline data collection to fully online systems with continuous measurement, biopharmaceutical companies attain a more intricate and comprehensive understanding of the process. This approach links critical process parameters to critical quality attributes, unveiling hidden process errors, such as lactate accumulation control crucial for cell health and viability. Additionally, it enables a more adaptive and dynamic approach to cell culture management.
Through the adoption of AI, ML, automation, and real-time monitoring of key process parameters, biopharmaceutical companies can accelerate process development, satisfy regulatory requirements, reduce errors, and ultimately deliver lifesaving therapies to patients with greater speed and efficiency.
3PBIOVIAN's main objective in enhancing accessibility to AAV-based medicines is to concentrate on offering the best-in-class AAV manufacturing platform that includes all aspects of the AAV program. We oversee all components in-house, covering vector design, plasmid production, producer cell banks, AAV stability and process optimization, process scaling, GMP manufacturing, an extensive analytical assay portfolio, and fill and finish. By adopting a holistic platform approach and simultaneous control over all aspects, we can enhance productivity, lower overall costs, and accelerate the manufacturing process.
Applying agile manufacturing principles and leveraging technological advancement, 3PBIOVIAN can enhance operational performance and thus speed of product delivery. Improvements start from broad and advanced process controls, design of experiments, implementation of novel materials, and integration of operating units during USP and DSP, ending at in silico manufacturing process scaling simulation.
3PBIOVIAN is investing in advanced process development techniques to streamline the manufacturing process. This involves high-throughput technologies, process intensification, and advanced analytics to optimize production processes, resulting in reduced development timelines.
3PBIOVIAN is known for its expertise in providing tailored solutions for gene therapy, recombinant proteins, and plasmids. We emphasize the use of innovative technologies and a customer-centric approach to meet the specific needs of our clients. By leveraging our expertise backed by 400 batches released and 20+ years of experience, we ensure an accelerated timeline while upholding the most robust quality standards.
A common approach in drug development is to discover new targets and mechanisms that can treat disease in ways that have not been done before. Historically, it has taken more than 10 years for the average biologic drug to go from early discovery to reach the market, at a cost of well over $1 billion.
An alternative strategy is to develop technology platforms for well-validated drug targets and classes where development of new drugs has been limited by technological barriers. For example, G protein–coupled receptors (GPCRs) are transmembrane proteins that are found in nearly all organs and tissues and carry out critical physiological functions in the body. There are more than 270 potential GPCR antibody drug targets spanning a wide range of indications, including cancer, inflammation, and pain. Despite being high-value therapeutic targets, GPCRs have been largely intractable to conventional antibody discovery technologies: only a few antibody therapies targeting GPCRs have been approved.
The deep complexities in structure and function of GPCRs and other high-value and well-validated targets, such as ion channels and peptide-MHCs, present challenges at each stage of discovery and development. Investing upfront in teams and technologies that specifically address these challenges could unlock these drug targets and provide platforms for repeatedly generating high-quality drug candidates across multiple programs.
This approach is already accelerating the development of significant new therapeutic opportunities across many disease indications, including cancer, metabolic and endocrine conditions, autoimmunity, and more.
In the dynamic landscape of biopharmaceuticals, where the urgency to transition from drug discovery to market delivery intensifies, the industry seeks innovative solutions to expedite product development and commercialization without sacrificing quality. Agilent’s commitment extends beyond laboratory automation; we leverage our comprehensive suite of biopharma workflow solutions, including advanced chromatography, mass spectrometry, spectroscopy, cell analysis, and genomics, to address these challenges effectively.
These innovative technologies are integral to optimizing drug development, from initial target identification to regulatory compliance. Harnessing the power of high-throughput screening systems enables our customers to rapidly evaluate thousands of compounds, significantly speeding up the initial stages of drug discovery. Efficiency in identifying viable therapeutic candidates propels promising drugs into the development pipeline more swiftly, shortening the path to market.
Agilent’s approach integrates AI with our state-of-the-art laboratory automation platforms and biopharma workflow solutions. AI's capability to sift through and analyze the vast data sets generated by these automated processes provides predictive analytics that refine experimental conditions in real time. This innovation not only accelerates development timelines but also guarantees the superior quality of both data and resulting compounds. Our pursuit to streamline the development process without compromising quality or scalability remains a focal point. Our scalable automation solutions, complemented by our comprehensive biopharma workflow technologies, ensure a seamless transition from lab-scale experiments to full-scale production. This scalability is pivotal in maintaining consistency, efficiency, and quality at every development phase.
Cell and gene therapy (CGT) represents the white-hot cutting edge of medical technology. Lentiviral vectors used in chimeric antigen receptor (CAR)-T therapy, plasmid DNA, and Adeno Associated Virus (AAV), as well as more experimental vectors, such as extracellular vesicles (EVs) all show tremendous potential. However, viral vector manufacturing is not yet standardized across the industry, with biopharma companies using different production systems and downstream processes. Standardization of the manufacturing processes would assist in accelerated product development and commercialization.
Adherent transient-transfection systems are being replaced with more scalable suspension systems, where the homogeneity of cell cultures can be improved but require different purification techniques. The purity of the viral vector itself is a function of the quality of the analytical process used to measure the purity. Examples of significant advances in this area can be seen for AAV processes, where CD-MS, ddPCR, and SV-AUC have allowed better characterization of the empty/full capsid ratio.
The costs of CGTs are high; a significant part of the cost can be attributed to the purification process. Key advances in downstream processing are also seeing appreciable improvements in terms of process compression gains, such as reduced time, cost, and waste, through the use of specialized purification media that has been designed precisely for the purification of these larger modality CGT targets. A great example of this is the LentiHERO®, ExoHERO®, and pDNAHERO®, technologies that are now on the market. These advances are noteworthy and are increasing the successful, routine commercialization of safe and reproducible CGT products to the patient.
Accelerating product development and commercialization without compromising quality is an ongoing goal within the biopharmaceutical industry. For example, at BASF, we are actively collaborating with customers and the industry to address the challenge of reducing variability in raw materials. By utilizing high-quality processing aids and excipients, troubleshooting can be simplified, thereby accelerating product development. Both the biopharma manufacturing processes and the API itself are highly complex, making troubleshooting particularly difficult, especially in the development stage where processes are less understood. Reducing the number of factors to consider by using consistently high-performing raw materials facilitates this process.
Additionally, we actively work with customers to develop new processing aids and excipients for biopharma processes. The objective is to improve the efficiency and robustness of these processes by introducing new materials. The formulation of biomolecules can potentially benefit from the development of new excipients, enabling new routes of administration or longer stability times.
We also strive to maintain close relationships with drug-developing biopharma companies, acting as a strategic partner rather than merely a supplier. Being involved early in the development processes allows us to anticipate and meet raw material needs and ensures that the most suitable and high-performing materials are selected, preventing delays later in the development process. Offering comprehensive support in material selection, including an overview of available options, their advantages and disadvantages, and even regulatory support in some cases, can greatly benefit biopharma companies and help streamline their drug development process.
The biopharmaceutical industry is actively adopting numerous strategies to form holistic approaches that combine agility, data-driven insights, collaboration, and risk management, enabling accelerated product development while upholding quality standards.
One key strategy involves the use of advanced analytics, where leveraging big data, ML, and AI enables companies to quickly gain insights from data. AI, in particular, is gaining prominence in the design of biological and small molecule drugs. Collaboration and partnerships are also fundamental, with biopharma firms extensively working alongside research institutions and other industry players. These strategic alliances help speed up development timelines, reduce costs, and provide access to specialized expertise. Another innovative approach is the adoption of parallel processing and integrated workflows, which contrasts with traditional sequential development. Companies are optimizing manufacturing processes concurrently with ongoing clinical trials and integrating workflows to ensure smooth transitions from discovery through clinical development to commercialization.
To navigate the regulatory landscape more efficiently, companies are leveraging expedited regulatory pathways, such as breakthrough designations offered by regulatory agencies. The industry is also adopting risk-based quality management, focusing efforts where they are most impactful to ensure safety and efficacy while minimizing time-to-market.
In manufacturing, the adoption of advanced technologies, like single-use systems, continuous manufacturing, and process intensification, is enhancing efficiency. Modular facilities further contribute to rapid scale-up and flexibility. Supply chain optimization is another critical area, with companies investing in redundancy, diversification, risk mitigation, real-time monitoring, and predictive analytics to prevent disruptions. Companies are also beginning to strategize around commercialization early in the development process, taking into account market access, pricing, and reimbursement considerations to ensure a successful launch.
One area that is top of mind for me is the need for process standardization for cell and gene therapies. A significant streamlining of manufacturing and production protocols shall not only ensure consistency and reliability in the development of these groundbreaking therapies but will also enhance quality and efficiency, reduce costs, and accelerate the translation of research findings into clinical applications. Collaboration among industry leaders, regulatory bodies, and translational and research centers will play a crucial role in shifting the focus from discovery and development toward manufacturing and commercialization of these therapies. The future holds the promise of a more robust and scalable framework for cell and gene therapy production, marking a transformative leap toward the widespread integration of these revolutionary treatments into mainstream healthcare practices.
Reducing time-to-market for biopharma companies remains a critical driver of program value. An emerging strategy that we hear about quite often recently has been the adoption of AI and ML into the drug development process, by which innovators aim to improve and/or shorten discovery and development timelines. While many have incorporated some element of these into their overall strategy, we are still quite early in assessing its true economic imprint in drug discovery and development. Beyond AI/ML, another approach to drive greater drug development efficiency is casting a greater focus on the end-user much earlier in the drug discovery and development cycle, which in our case is the patient. By starting with unmet needs of specific patient populations and then working backwards to develop the product candidate attributes and clinical roadmap to address those needs, scientific and cross-functional teams can become quite tactical and deliberate against the natural metamorphosis along each step of the drug discovery and development process –– from target validation through drug labeling –– with a clear tangible end goal. In conventional drug discovery and development, scientists often start with a breakthrough scientific finding –– a new target/pathway –– which can be incredibly exciting and relevant scientifically, but then spend years towards developing a potential solution that ultimately searches for a real-world problem. Building teams that can incorporate a patient-first approach into scientific, target-first conversations can not only accelerate the Gantt chart but should contribute to overall program probability of success and the creation of more first or best-in-class target product profiles to truly impact human health.
Within clinical trials, the use of a synthetic control arm can increase efficiency, reduce delays, and lower trial costs, ultimately bringing lifesaving therapies to market faster. Generated from RWD previously collected from external sources, such as medical records, the synthetic control arm reduces the need for data from control participants assigned to a placebo group. As well as the economic benefits, there is a reduced concern for patients that they will be randomized to the placebo group.
Of course, it's important to remember that information from RWD sources may be difficult to extract or of poor quality. In order to generate research-quality evidence that ensures confounding variables are accounted for, new tools and methods that will consolidate, organize, and structure the data are needed. Despite these challenges, the value of synthetic control arms in drug development are clear, and they can provide a high benefit-to-risk ratio in the right situations. In order for their use to become more widespread, synthetic control arms could be used as a comparator in phase II trials for internal decision making –– a move that is both commercially viable for sponsors and appealing to drug developers.
BioSenic plans to use RWD for some of its next rare disease phase II clinical trials with small patient populations to speed up translation. Synthetic control arms don't solve all the challenges of randomized trials, nor do they realize the full promise of real-world evidence in drug development. But they are an excellent starting point.
We partner with top-tier CMOs known for their exceptional quality and reliability, delivering superior cost-to-quality rather than only costs –– a common trend in the industry. This approach allows us to balance high quality and time-to-market. Additionally, we carefully consider geopolitical factors and minimize our reliance on Chinese suppliers.
All of this comes at a cost, but we are convinced the pharmaceutical industry needs to move to metrics of cost-to-quality and cost-to-time-to-market. The current model of "cost-at-all-costs” has proven unreliable, is prone to creating repeated quality issues and market shortages, and is ultimately not benefiting patients, only the pockets of insurers and wholesalers.
The field of advanced therapeutics is rapidly progressing and brimming with innovation, offering great promise for patients who are battling life-threatening diseases. We are witnessing ongoing benefits from platform approaches in both viral vector and CAR-T process development, production, and analytics. A standardized platform approach enables rapid progression to first-in-human studies, accelerating the development process while ensuring safety and efficacy. Moreover, a platform process streamlines the path to commercialization, ensuring that life-saving treatments reach those who need them most in a timely manner. The benefits of these approaches are far-reaching. They not only enhance efficiency but also foster consistency and reliability throughout all stages of therapy development thus ensuring quality at every stage.
Catalent has developed standardized platform processes tailored to CAR-T cell therapies and adeno-associated virus (AAV) vectors, which are among the most prevalent modalities in cell and gene therapy. The UpTempo℠ manufacturing platforms introduce a flexible production environment for cell and gene therapies. This environment is fully modular and flexible based on key unit operations, allowing innovators to transfer previously developed processes directly into the UpTempo ecosystem, or leverage Catalent’s suite of standardized workflows and platform analytics, which have been optimized for efficient manufacturing of advanced therapies. The result is streamlined process development, efficient manufacturing and quality control, and the delivery of life-changing therapeutics to patients faster.
Multiple strategies are being utilized within the biopharmaceutical industry to reduce time-to-market and accelerate access for patients to innovative therapeutics. One of the most prominent is the use and deployment of platform technologies, such as CRISPR in the development of multivalent vaccines. Another example is the adoption of technology that drives industry standardization and streamlines the chemistry, manufacturing, and controls (CMC) strategy for CGTs. Several programs are being developed by the FDA to support the adoption of platform technologies by therapeutic developers, including the Advanced Manufacturing Technologies Designation Program (AMTDP) that stems from the Prescription Drug User Fee Act (PDUFA) VII commitment letter and the Federal Food, Drug, and Cosmetic Act and the Platform Technologies Designation program that stems from the PREVENT Pandemics Act. Both programs are specifically designed to increase efficiencies and expedite the drug development life cycle while ensuring that drug products have equivalent or superior drug quality. Ultimately, the use of platform technologies within the biopharmaceutical industry will play a key role in reducing time-to-market and enable therapeutic developers to reliably deliver safe, efficacious, high-quality, and cost-effective drug products for all patients in need.
The clinical phase is the longest part of a drug development and continues to increase, due to the ever-extending patient recruitment period. The reasons are mainly related to the size of the cohort, the increasingly complex protocols, and the limited resources of recruitment sites.
The introduction of innovative statistical methods, surrogate markers, and intermediate endpoints helps increase the statistical power of the study, reduce the number of patients to be treated, and shorten the required follow-up time until market approval. The use of RWE as an external control group is also gaining ground and can reduce recruitment duration.
Another strategy of acceleration is to reduce the workload on recruitment sites and the burden on patient care. Patient data should be collected at home or in a nearby facility. The integration of potentially less-experienced recruitment sites that are more accessible to patients requires greater support from the sponsor but can facilitate recruitment later on –– all the more so since sponsors tend to tighten the inclusion criteria of patients to maximize the chances of treatment success.
Understanding the cellular mechanisms behind a pathology is often an iterative process that can take time, considering the complexity of cellular activities and possible chemical interactions. The screening of therapeutic targets thus consists of a slow process of trial and error. This can be optimized today through the combination of new technologies, such as high-throughput sequencing and deep learning, which can process large amount of ‘omics data and enable a faster assessment of potential therapeutic targets.
I can speak to the strategies Charles River is employing to accelerate product development to enable therapeutic developers to reach a clinical value inflection point more quickly. In the advanced therapeutics (cell and gene therapy) space, Charles River leverages a “concept-to-cure” portfolio to streamline the journey for therapeutic developers, from development to clinic, bringing potentially curative, life-changing therapies to patients. While these goals are many times program-specific, the overarching goal is to cut up to 12 months from the therapeutic development timelines across the preclinical and development activities leading to clinical drug product manufacturing by utilizing capabilities all under the Charles River umbrella.
Development is often hampered by an inability to scale. While Core Biogenesis isn’t developing therapeutic biological drugs yet, from the beginning we have focused on scalability to produce recombinant protein, most of which will be used to manufacture CGTs and other biologics. These proteins are made in plants that can also be grown in open fields, meaning we have no limit of scalability compared to conventional production methods.
The supply chain for raw materials is of course essential in any industry but has been a particular problem for CGTs. This is due in part to the field’s relatively young age, meaning a still-growing stable of suppliers has been racing to keep up with demand, which is troublesome for stability and rate-limiting when developers look to scale up. There are also challenges related to geography, including the different standards held by various regulators.
This is why we focused on developing plant-based products to ensure stability, consistency, and on-demand scalability. Like animal cells, plant-based processes still require a strong quality management system and clear quality control specifications for our product, but plant-based systems don’t have the same contamination risks.
Core Biogenesis was founded to provide the pharmaceutical industry unmet assurance of supply for recombinant sustainable growth factors and cytokines, in order to reduce these common time-to-market pressures.
Having platform processes provides a common approach to the process development of mAbs. But if we are to realize a step-change improvement in the development times of mAbs, we’ll need to reduce the bottleneck caused by physical experimentation. I see great promise in accelerating the use of digital tools. If we build on our current sensor technologies and advance AI/ML model building, we could reduce the timelines to generate the needed data sets, as well as improve process performance.
When it comes to CGTs, the development is often spearheaded by smaller biotechs and translational groups pushing towards clinic. The combined resources needed to develop new clinical programs while simultaneously building new, high-performing production platforms poses a significant risk to the success of these innovators. This hampers near term accessibility and affordability of these transformative treatments to larger patient populations. To address this, Danaher has partnered with the Innovative Genomics Institute (IGI) to yield a “stacked” manufacturing platform for gene-editing therapies, starting with targeting two rare diseases and eventually broadening the scope. Developing a platform that uses technologies and solutions from Danaher operating companies, including Cytiva, will enable the scaling of CRISPR-based therapies. Having a platform process for cell and gene therapies would enable treatment availability for thousands of patients, not hundreds.
The extensive path to market, inherent in new drug development, can take over a decade. Today, drug developers can focus on a few strategic levers to accelerate the product development process.
Early selection of the optimal manufacturing technologies is critical in reducing time-to-market. Manufacturing technologies that are scalable by design are essential to rapidly advance from small to commercial scale, as they effectively eliminate the need for prolonged and expensive process optimization phases. For instance, the Univercells Technologies scale-X™ bioreactor enabled a scale-up of adenovirus production from R&D to GMP commercial scale in eight months.
Integrated and automated manufacturing technologies, such as the NevoLine™ Upstream platform, also from Univercells Technologies, can shorten processing times without compromising process robustness. By eliminating intermediate steps and reducing the inherent variability associated with manual operations, these manufacturing platforms simplify processes while maintaining process reproducibility.
Outsourcing to contract development and manufacturing organizations (CDMOs) can bring the necessary expertise and capabilities for developers to navigate this complex landscape and guide them through these decisions. CDMOs with established platform processes further contribute to accelerating the process development and scale-up phases of a drug pipeline.
Anticipating the regulatory strategy, by planning early and engaging with experienced stakeholders and regulators, can provide valuable guidance to developers, helping them design the most suitable regulatory pathway. For instance, considering expedited programs offered by the FDA and EMA to accelerate the approval process of CGTs or opting for adaptive clinical trials that allow for modifications based on interim data analysis are among the options available to minimize timelines.
Reducing overall cycle times while maintaining or improving quality is a critical priority right now across the life sciences. The average total cost to bring a drug to market continues to heavily increase, with a recent study from the Tufts Center for the Study of Drug Development stating the number at over $2.1 billion, a result of the increasing complexity of clinical research. Both scientific and technological advancement have exploded in recent years, but this has also in many cases created an explosion of data types and data streams and protocol complexity, as well as silos, resulting in duplicative data processes. Data, technology innovation, and strategies focused on data oversight and a single source of truth for data can unlock efficiencies and enhance quality, taking this deluge of data and using source-agnostic ingestion and standardization capabilities to seamlessly connect data acquisition, data infrastructure, and analytics. Adopting AI and risk-based approaches to remove the manual and stepwise processes that persist in clinical research are critical steps for reducing timelines while addressing data quality at scale. It starts with having an interoperable, scalable architecture that sets the foundation for AI and generative AI (GenAI).
With the challenges of the biotech market over the last couple of years and the ever-growing need for novel therapies, there’s been an increased effort to figure out ways to accelerate drug development timelines across the board. There are a few strategies that companies have used to try to achieve this goal. The first is rethinking how we design clinical trials. It’s now become common to see adaptive design and interim analyses. Companies are also doing extensive market research to figure out what physicians/payors are looking for before launching a trial to save on time and cost. Part of the research involves speaking to KOLs about dosage, systemic versus local administration, etc. In addition, there are conversations about creative approaches to recruitment strategies to accelerate enrollment.
Outside of these strategies, I’ve seen companies be much more proactive about reaching out to the FDA and other regulatory agencies to have regular conversations about updates/feedback. This helps save time down the line since, in the past, assets would reach NDA stage but not be able to get approved because of problems that could have been addressed early on.
Lastly, with the significant difficulties in fundraising, companies have turned to partnering assets to help reduce costs as well as shrink timelines. This is a particularly effective strategy that allows two companies to pool resources to get much-needed therapies to patients faster.
Small molecule medicines offer significant advantages to patients and healthcare systems — of the FDA-approved drugs to date, more than half of them are small molecules. They are convenient, easy to make and administer, can be dosed in multiple ways, and are often much more affordable than other therapeutic modalities. In short, chemistry makes great drugs. Yet, conventional small molecule drug discovery has long been an unpredictable, lengthy, and difficult process –– relying on synthetic libraries and target-driven approaches and unpredictable toxicity.
If we focus on the chemistry designed by nature as a result of millennia of co-evolution between microbes and humans, we can speed up the drug discovery and development process. New advancements in genetics, synthetic biology, AI, and machine learning allow us today to quickly identify novel compounds encoded by DNA and associate them with human health and disease.
Taking an inside-out approach and generating chemistry from the genetic code can drive a potential deep-reaching transformation in drug discovery, making promising small molecule drug leads in a fraction of the time it normally takes.
Importantly, because these molecules are found in healthy individuals, they are prequalified and physiologically compatible for human use, creating the possibility for an accelerated path to drug candidates with a higher clinical probability of success. Going from observation to potential candidate makes drug development faster –– and potentially more efficient and safe –– than conventional approaches.
Find better starting points. First, finding good starting points at various points along the pipeline –– for a molecule, a solvent, etc. –– can have a big impact on how long the drug development process takes. Many labs still rely on human memory or scattered notes to find starting points –– that is, people recall or manually search for similar projects that have been executed in the past to find starting points for a new project. A first step to improving this part of the process is to collate and organize all the data so anyone in the lab can easily and quickly find the "best match" starting points in a database. A second step is to use AI (usually machine learning) to recommend a starting point based on the past data points plus the goals of the project, but instead of just doing a "best match" lookup, the AI can use inference to recommend a superior starting point that has never been used before. These systems not only save time with finding a suitable starting point, but if the starting point is better, then it can also reduce the number of R&D iterations required to find a suitable result. This need for a starting point is repeated many times throughout the drug development process and so accelerating the finding of starting points –– and finding better candidates –– can have a significant impact on the overall time it takes to develop a drug.
While many strategies likely surfaced to accelerate product development and commercialization in biopharma, it seems the priority or likely hottest opportunity at the moment is to prioritize dealmaking, pending you have the capital or the ability to create capitalization to fund. With last year’s pharma M&A, the strongest since pre-COVID, M&A will continue to increase as pharmaceutical companies look to bolster their drug pipelines with buyouts amid the race to secure competitive long-term therapeutic markets, backfill ailing mature drugs to patent expirations and generics, and pursue new revenue streams. According to a 2023 study done by IQVIA, key findings show significant spending growth areas expected over the next five years in oncology, obesity, and neuroscience. It will be important for drug manufacturers to balance “buying into growth” versus a number of factors including but not limited to the number of generics and biosimilars in market, the federal tightening of policy to control drug prices via the Inflation Reduction Act (IRA), and ultimately the capitalization and time required to bring early and preclinical drugs in development to market.
Much of the focus in market acceleration has been on the use of AI. There have been claims that the life sciences industry is behind other industries in its adoption of AI; however, I believe the focus has been skewed: the use of AI has been primarily used with chemistry for the acceleration of drug development, especially for cancer.
Rather than focus on AI for chemistry, where we are trying to find better binding sites on known targets, we should be using AI in biology to discover new modalities and novel targets and to understand resistance mechanisms. This will ensure we can deliver even more impactful results by, for example, looking at the development of cancer tumors and how multiple metabolic variables interact with tumors.
Further, this opens up many therapeutic options, such as leveraging metabolic synthetic lethalities to open up entirely new biological mechanisms by which to treat tumors. The variables are vast, and using AI to discover new biological mechanisms is perfectly suited to this task. It can handle unlimited variables, run complex and multiple scenarios, and do this in a fraction of the time.
The acceleration of AI with chemistry is already massively reducing time-to-market, but we can do more if we shift to using AI tools, to discover new insights into disease biology. It can open therapeutic options that have never previously been possible and allow scientists to employ new strategies, to test them thoroughly outside of the patient and to accurately predict individual patient response at the molecular level.
Thinking about manufacturing at scale from the outset of product development is critical to reducing overall time-to-market. Actively making decisions about manufacturing equipment, raw materials, and assays as early as possible in the development process is essential. The use of scale-down models of the manufacturing process can be very beneficial. This approach helps to avoid unnecessary loss of time spent on process development and (re-)validation in the later stages of product development, which is often a critical factor in maintaining project timelines and budgets.
Particularly for cell culture–based products, the choice of manufacturing equipment is critical to the quality and yield of the final product. For therapy developers, this means that going from T-flask to bioreactor after phase II trials is by far too late. For manufacturing equipment suppliers, the emphasis on providing scale-down models that ensure linear scalability has become increasingly important. These models are critical in facilitating a smoother transition from laboratory-scale to full-scale production, ensuring that process scalability is maintained without sacrificing product quality or efficiency.
In addition, the adoption of platform processes provides a valuable opportunity to further reduce time-to-market. By using a consistent cell line across different products and applying the same or similar process parameters, it becomes easier to predict performance during scale-up. This approach can reduce the need for additional characterization and process optimization, saving significant amounts of time and resources. The ability to predict how processes will perform at scale, based on established platform processes, enables more efficient planning and execution of product development strategies, ultimately leading to faster delivery of new products to market.
There are only a couple of key levers at pharma’s disposal to get drugs into the patients that need them faster: (a) create more “shots on goal” by bringing more drugs into clinical stage or (b) maximize the likelihood of those shots being successful. Clinicogenetic data are critical for both of these since drug targets with genetic validation have a significantly higher chance of making it through the drug development gauntlet.
Genetic data combined with linked clinical data can optimize target discovery and address the challenges of precision medicine development. It can help us better understand complex disease mechanisms, which enable the development of targeted therapeutics with better safety and efficacy profiles.
Linked clinicogenomic data also support the identification and stratification of targeted diverse patient subgroups that are more likely to respond to targeted therapeutics. De-identified genomic data from a diverse subpopulation can drive the discovery of novel genetic associations, which would not otherwise be possible.
At IGC Pharma, we have designed a vertically integrated business model to reduce our time-to-market and drive quality control. In addition to our headquarters in Potomac, Maryland, we have a manufacturing and processing facility in Vancouver, Washington and a research and development (R&D) facility based in Bogota, Colombia. These facilities integrate the key elements of our supply chain to help ensure the best quality product for our trials while simultaneously enhancing time and cost efficiencies, particularly as the company grows.
Our lead drug candidate, IGC-AD1, has the potential to revolutionize the treatment of Alzheimer’s Disease as the first and only low-dose natural THC-based formulation candidate currently undergoing FDA trials. Rather than depending on third parties, with our R&D facility in Bogota, we produce high-quality legal cannabis efficiently for our clinical trials. As we continue to progress IGC-AD1 through FDA trials, we’re readying these facilities for both phase III testing and commercialization. Ensuring we have the correct licenses and approvals will avoid any delay in manufacturing.
Second, we are integrating artificial intelligence in our trials to accelerate product development. As part of our phase II clinical trial, we have partnered with the CINFONIA research institution at the University of Los Andes to leverage generative artificial intelligence (AI) to analyze variations in disease signatures among patients, enabling us to identify the most effective dosages for patients and subsequently accelerate treatment delivery. We’re hoping to increase the efficacy of our formulations, paving the way for an AI-led wave of precision medicine.
All these strategies help reduce time to market.
Companies are taking a multifaceted approach as they try to accelerate product development timelines during this time when there is a lot of innovation but limited capital to fund it from early R&D to commercialization. One way companies have shrunk timelines is with advances in automation, which makes production faster, and the advent of AI, which has helped companies process large volumes of data. Partnering is another important strategy that’s been popular in the last couple of years. What may take a small biotech 10 years to develop can potentially be done in half the time with a strong partner who has the infrastructure, funding, and expertise to move an asset forward.
In addition, hiring the right team with the expertise in the technology and indications you’re working on is essential in all of this as well. If you have the right people in place, things tend to go more smoothly, and there are fewer hiccups that can slow things down.
In terms of what we’re doing at KBio, we’re always thinking about how to be efficient while maintaining quality. We have a plant-based platform where we’re able to produce biologics significantly faster than traditional production methods. We can go from DNA to GMP material in an unprecedented 8–10 weeks and can easily switch between the production of different products as needed. This kind of speed and flexibility makes a significant difference in the overall development process.
The cost and complexity of chimeric antigen receptor (CAR)-T cell therapy manufacturing
represent challenges that must be overcome to improve access to potentially lifesaving
medications. The successful approach to product development builds on established practices and rapidly innovates when promising data emerge. This is particularly applicable for processes involved in the manufacture of cell-based treatments whose cost creates barriers to patient access and ease of use. There is a need for innovative and efficient solutions to scale up production of these labor-intensive processes and maintain batch-to-batch reproducibility while reducing cost and complexity for broader treatment affordability.
As an example, leukapheresis, a form of cell collection used in CAR-T cell therapy, is invasive and requires time, with high costs and resource constraints. Patients are connected to a cell separator for hours to harvest T cells, which are then genetically engineered in a lab to express the CAR and then reinfused into the patient’s body. This process is expensive and slow. Once promising clinical data emerges, the implementation of novel technologies for process minimization –– such as Kyverna’s Ingenui-T –– may address cost, challenges for access, and ease of use, while preserving the therapeutic value. Because this innovation holds significant potential and may eventually lead to eliminating the need for leukapheresis, thus improving the overall patient experience, the ability to rapidly make this product commercially available could be a major milestone in the ongoing efforts to optimize cell therapy delivery, facilitating the rapid advancement of product development pipelines and ultimately, improving patient care standards.
One unexpected outcome of the COVID-19 pandemic was that the biopharmaceutical industry, when properly focused, can bring new drugs to market very quickly without sacrificing quality standards. The industry took away several strategies in an effort to continually reduce the time-to-market.
As in other industries, high hopes are pegged to the adoption of AI/ML, high-throughput screening, and computational modeling. If we can properly utilize them, these tools should enable faster decision-making. Similarly, advanced manufacturing technologies like continuous manufacturing, single-use systems, and novel PAT enhance manufacturing efficiency, flexibility, and scalability.
There are also inventions specific to our field, including promising new approaches to drug encapsulation and delivery that can make it simpler to develop, test, and ship new therapies.
Companies have learned to optimize collaboration with each other, leveraging complementary capabilities and sharing in the outcomes. This also extends to regulatory agencies, where proactive early engagement fosters alignment and reduces risk.
As market conditions soured, we learned the importance of strategic portfolio management and agility to further drive efficiency across the product development life cycle. Companies now apply portfolio management earlier in a project’s life in order to free up resources for more appropriate projects. In addition, flexible methodologies, iterative prototyping, and rapid cycle times enable quick adaptation to changing market dynamics, patient needs, and scientific advancements.
We have made significant strides, but must stay focused on the unmet needs still ahead of us that require us to move even more quickly and efficiently.
The evolution of CRISPR gene editing from a research tool to therapy is unprecedented, with the first CRISPR-based treatment reaching the market at the end of last year. Initially for genetic diseases, CRISPR has the opportunity to expand into other markets, like oncology and infectious diseases. With forthcoming drug approvals expected to rapidly reach the market in the next several years, the focus now lies on refining CRISPR, improving specificity and target editing.
CRISPR genome editing, while powerful, presents new challenges that may impact the clinical safety and efficacy of gene-editing–based therapies. In particular, CRISPR editing results in a mixture of intended and unintended outcomes (on- and off-targets), as well as differences in the zygosity of edits. Traditional methods of measuring these outcomes are time-consuming and laborious, which can significantly increase R&D costs, lengthening the drug development process.
Single-cell multiomics offers CRISPR drug developers a means to evaluate cell population diversity, specifically characterizing indels, measuring zygosity, and predicting off-target effects faster and more comprehensively than conventional technologies. This approach enables researchers to dive deeper into early potential indicators of the safety and effectiveness of gene-editing therapies, which could ultimately lead to improved patient outcomes. By integrating single-cell multiomics throughout the development process, companies can better mitigate risks, gain detailed insights, and increase the chances of successful drug approval and market entry.
The number one priority for biopharmaceutical researchers we speak with revolves around reducing time-to-market for genetic medicines, including mRNA therapeutics, cell and gene therapies, and CRISPR-based treatments, and as such, our ability to write or synthesize DNA quickly has never been more critical.
Traditional phosphoramidite DNA synthesis, often called the “chemical method,” uses harsh acids and bases that damage the DNA and limit current synthesis capabilities to either short or long, inaccurate pieces. Scientists are often waiting weeks to months to get their desired sequences, delaying experimental findings and adding costly time to the development and commercialization of genetic medicines.
Enzymatic DNA synthesis technology enables the production of long, high-fidelity DNA sequences. This technology not only eliminates the inefficiencies associated with traditional chemical DNA synthesis methods but also can provide researchers with the precise genetic material they require. By doing so, it can significantly shorten research and development cycles and reduce costs, thereby accelerating the pathway from laboratory to market for next-generation genetic medicines.
This shift toward the fully enzymatic synthesis of DNA represents a strategic approach to expediting product development within the biopharmaceutical sector. It ensures that the journey of bringing advanced therapeutic modalities from conception to commercial availability is both faster and more cost-effective without compromising the quality or integrity of the DNA used in these critical applications.
With more than 90% of drug candidates failing in clinical trials, the industry is in desperate need of accurate biomarkers that can better stratify patient populations. Leading biopharma companies now have the opportunity to utilize AI for biomarker discovery and development to inform which molecular and cellular signatures predict patient treatment response. Taking this approach could expedite the overall drug development timeline and reduce R&D costs.
Automated analysis of biological data using AI enables rapid extraction of novel patterns and features from patient biopsies. This automation ensures precision and reproducibility. Furthermore, AI-driven biomarker algorithms aid in the identification of novel patient subpopulations, potentially expanding clinical trial enrollment to previously unrecognized groups who may benefit from a particular treatment.
With a patient diagnosis, especially in cancer cases, the conventional method involves tissue biopsies followed by pathologist examination of specimen slides. However, through advancements in AI-powered spatial biomarker technology, we can unveil the cellular landscape of cancer, turning a static biopsy slide into a dynamic AI-guided action plan with information that can help clinicians make informed data-driven decisions.
At Nucleai, we believe that more reliable therapeutics and diagnostics can be achieved by mapping the cellular interactions within the tumor microenvironment using spatial AI for companion diagnostic development, especially in clinical trials evaluating emerging modalities like immunotherapies, antibody–drug conjugates and bi-specifics. Using this approach, companies can expedite the delivery of innovative treatments through faster identification of viable drug candidates and more efficient clinical trials.
As the CEO of a radiopharmaceutical CDMO, I am acutely aware of the challenges and opportunities in meeting the needs of product development and commercialization. As a CDMO, we are pursuing several strategies that will address the development and manufacturing issues our biopharmaceutical clients experience while doing so with the highest quality standards.
First, we are conducting thorough risk assessments and limiting development activities to streamline the product development process. This focused approach allows us to prioritize resources and efforts on the most promising projects. Second, we are using process and analytical platform approaches to standardize and expedite development activities. By employing these platform approaches, we can reduce variability and increase efficiency in our processes. Third, we are utilizing technologies, such as single-use systems and automation, to shorten our timelines. These innovations enable us to scale up production quickly and reduce the risk of contamination, which is particularly crucial in the radiopharmaceutical field. Fourth, we are leveraging product portfolio synergies to expedite the go-to-market process. By identifying and capitalizing on synergies between different products, we can streamline development and commercialization efforts. Finally, we are establishing key partnerships for optimal distribution and adoption. Strategic collaborations with other companies are essential for ensuring that our products reach the market efficiently and are adopted widely.
By employing these strategies, we aim to expedite the time-to-market for our radiopharmaceutical products while maintaining the necessary quality and compliance standards, ultimately benefiting patients and healthcare systems worldwide.
One of the most promising paths being applied to accelerate product development and commercialization comes in the form of real-world data/evidence (RWD/RWE) and applied AI. In clinical development, we are using de-identified RWD to identify clinical sites with eligible patients and using RWD to test clinical trial protocols and to identify potential blockers in inclusion/exclusion criteria. More groundbreaking is using RWD/RWE to construct and deliver synthetic control arms, greatly reducing the time it otherwise would take to recruit and randomize patients to have a sufficient control arm. With respect to commercialization, the combination of RWD and AI offers the opportunity to improve time to diagnoses, personalize treatments based on what is predicted to be most effective, and identify patients who are part of the “treatment gap” –– meaning they should be on guidelines-based therapy but are not. At OM1, PhenOM, our AI-based personalized medicine platform, is driving all three use cases in the clinic today and with that advancing commercialization. These include identifying underdiagnosed patients with rare conditions like Fabry disease or more common but fatal conditions like abdominal aortic aneurysms, as well as predicting which patients are more likely to respond to specific therapies in obesity, rheumatoid arthritis, and major depressive disorder and finding undertreated patients with conditions like migraine and hypercholesterolemia. The promise of AI and RWD is proving to be the vehicle for the right drug for the right patient at the right time.
We have entered an age in drug development where new medicines are not discovered but engineered to predetermined specifications. The rise of modular platform technologies, particularly in the genetic medicine space, allow us to create programmable therapeutics that can impact cellular physiology with unprecedented specificity to address biological drivers of disease. Concurrent in silico drug development through the use of AI and ML tools enables us to prospectively engineer these emerging classes of therapeutics with increasing levels of confidence for success. This process is faster, reducing the need to “discover” drugs through time-intensive screens. Additionally, modular platform technologies create opportunities to build in safety at the core of the development process by enabling real-time monitoring, precise control, and quality assurance throughout, thereby reducing the risk of errors and potentially enhancing overall safety profiles.
In our field of precision epigenomic control, we leverage our OMEGA platform to rapidly and rationally develop epigenomic controllers. These programmable mRNA medicines modulate gene expression to predetermined disease-specific levels and duration with applicability across a broad range of disease processes. By reducing lead times, focusing on safety, and enhancing scalability, we see a bright future for platform technologies, such as Omega’s. These strategies can address the industry's appetite for speed but also contribute to fostering a more agile and adaptive landscape for future advancements in healthcare to bring safe and effective therapeutic options to patients.
The biopharma industry faces immense pressure to shave years off the traditional 10- to 15-year development timeline. Patients eagerly await treatments, but compromising quality is never an option. So, how do we balance speed and safety?
By partnering with raw material suppliers, CDMOs, contract research organizations (CROs), and other specialists, biopharmaceutical companies can unlock a wealth of expertise beyond their internal capabilities. These specialists offer industry knowledge, innovative solutions, and alternative approaches that can accelerate development and overcome internal roadblocks.
External experts can handle sourcing high-quality materials, managing clinical trials, or navigating regulatory hurdles, freeing up internal teams to focus on core research and development and maximizing efficiency and impact. Through outsourcing, companies can dedicate their resources and expertise to driving their unique value proposition forward, whether that's cutting-edge drug discovery, personalized medicine, or novel delivery systems. Leveraging external expertise allows companies to reduce time-to-market and development costs, increase efficiency and innovation, and focus on their core strengths and competitive advantages.
In addition to strategically partnering, companies can accelerate timelines by fostering collaboration among research and development, process development, manufacturing, and regulatory teams to ensure everyone is aligned and working toward the same goals and to avoid later-stage technology transfer and clinical manufacturing pitfalls. Furthermore, engaging with regulatory agencies early and often, seeking feedback throughout development, and utilizing pilot programs for innovative approaches can prevent delays later.
Several strategic initiatives are being employed to expedite product development and commercialization, especially in manufacturing, all without compromising quality.
There's a pronounced shift toward integrating platforms that simplify manufacturing, like the G-Rex® for CGT. Platforms like these streamline the production process by offering efficient operational workflows. The G-Rex system, for one, allows for a straightforward approach to CGT manufacturing by providing continuous oxygen access to cells, thereby reducing the complexities of mixing and feeding. This advancement significantly cuts down development time by optimizing the cell cultivation process.
Moreover, the industry is increasingly adopting principles from diverse sectors, such as automotive, medical devices, and even fast-food production, to standardize and automate manufacturing processes. This standardization across reagents, equipment, and workflows results in a more robust, repeatable, and scalable manufacturing process. Such standardization not only accelerates production but also ensures consistent quality across batches.
Collaboration is also key. In the CGT space, partnerships are fostering high-throughput, cost-effective manufacturing solutions. This collective effort is vital for streamlining production, enhancing efficiency, and ultimately reducing time to market.
Importantly, these strategies are further supported by strong collaborative efforts among academia, industry, and regulatory bodies. This synergy is crucial for ensuring that rigorous standards of quality and safety remain uncompromised while the pace of development accelerates.
The biopharmaceutical industry needs to be simultaneously leveraging technological innovation, standardization, and collaboration to quicken the pace of product development and commercialization, ensuring that lifesaving treatments are delivered swiftly and safely to those in need.
High-throughput strategies are very important, but in order for them to be effective, increased throughput cannot come at the expense of quality. Without high data quality, the incidence of false positives can be high, and failures can occur further down the process. At SCIEX, we work with customers to innovate breakthrough technologies that deliver precise and information-rich data at speed.
An example of such a breakthrough technology I’d like to share is our Echo® MS + System. We have coupled the speed of acoustic ejection sampling with the quantitative power of our flagship accurate mass MS system, the ZenoTOF 7600 system. Together, this provides the ability to run samples very fast –– at speeds up to 1 second per sample, but with high data quality and fidelity. Many technologies in the high-throughput space focus on speed alone, but we have focused on ensuring that the quality of the data is not compromised by the speed of acquisition. This means that, if we can get to answers faster with the high-quality data acquisition and processing, decisions and progress can be made faster, and chemical entities can progress faster through the drug discovery process.
Key to accelerating discovery, product development, and commercialization is removing barriers that historically limit each step. Over the past half-century, we have seen key innovations that have significantly advanced our understanding of the mechanisms of health and disease. Arguably, one of the principal advances over the last 30 years has been in the field of genomics. Genomics has in turn fueled other omics, such as the transcriptome and methylome. As we look forward, we anticipate that the next advance will be driven by a deeper understanding of the proteome, which is inherently more complex than the genome.
Proteomics, the study and functional analysis of proteins, can offer incredible insights at a molecular level. However, a major challenge has been the ability to scale the analysis of proteins within complex samples, such as blood plasma, to power studies large enough to tease out differences at a population level. By studying proteins at scale and depth with peptide-level resolution and an unbiased approach, researchers will gain novel biological insights into health and disease.
The Proteograph Product Suite working in tandem with mass-spectrometry is the only unbiased proteomics solution able to scale to a population level in complex samples. It provides an unprecedented depth of coverage and, importantly, peptide-level resolution, which enables the determination of variants or isoforms of proteins that may play distinct roles in health and disease. These innovations will offer drug developers a deeper understanding of the biology underpinning drug response and an opportunity to reduce time-to-market.
As a CMO, Selkirk Pharma’s utmost priority is the reliable delivery of quality products to the pharmaceutical market in the shortest possible time. Our strategy is building a purpose-built facility with infrastructure that enables the successful implementation of Pharma 4.0™ programs. These programs include deployment of digital solutions spanning quality, manufacturing, supply chain, engineering, and validation with a goal to remove paper-based data and process barriers. Implementation of our comprehensive Digital Transformation Plan will enable real-time data access, effectively removing delays between related clean and non-clean room activities, accelerating document review and deviation resolution, and providing more client transparency in the manufacture of their product. The benefits of Pharma 4.0 include review by exception and real-time QC and QA release, resulting in fewer factory disruptions and more reliable product delivery.
The use of electronic documentation reduces human error. Data are verified against control parameters at the time of data entry and will only allow progression if those controls are met. Advanced analytics help identify equipment and quality issues earlier in the manufacturing process. Errors are identified and addressed by the Quality team in real time, enabling immediate remediation of any potential quality issues, accelerating the review cycle, and shortening the timeframe between product manufacture and release.
The increase in quality achieved through the implementation of Pharma 4.0 accelerates our ability to rapidly bring our customers’ injectable drug products to market.
As a foundation for digitally-enabled manufacturing, we at SK pharmteco deployed a manufacturing engineering system (MES) from Autolomate to augment process development and manufacturing for both clinical and commercial operations. It delivers efficiencies and accelerates timelines in three ways:
Automating workflows: MES solutions are designed to streamline workflows by automating routine tasks and facilitating smoother transitions between different stages of the manufacturing process. This leads to reduced production cycle times, increased accuracy and throughput.
Bringing innovative medicine to patients faster by reducing time to market is every biopharma company's goal. As companies plan to accelerate their development timeline, it is critical to maintain the safety, efficacy, and potency of the product. Functionally closed and automated systems that can support unit operations of manufacturing processes in a flexible manner will enable safe acceleration.
The systems should allow the exploration of multiple variables in the early stages of development. As the product progresses through development, such systems should be able to facilitate continued compliance.
The systems should have current software capabilities, such as electronic data reporting, given the importance of collecting data throughout the manufacturing process. Together with automated systems, AI and ML tools can help researchers learn and advance manufacturing processes.
For example, biosensing tools can be employed to monitor cell culture parameters. The collected data can feed into machine learning models to optimize the parameters and systems. As the capabilities of the systems advance, the systems may be able to adapt processes and improve efficiency at each step.
Collaboration will be the key to harnessing the power of these systems. New, innovative solutions can be designed by sharing the learnings with multiple stakeholders in the industry.
TrakCel works with many CGT companies who are seeking a quick pathway toward commercialization. All of them understand the risk of implementing strategies that could negatively affect the quality of the drug product or its value chain management. Anything that compromises quality is a false economy and will not be entertained, as it will at best delay time-to-market and at worst profoundly affect patient safety.
The best strategies to accelerate clinical development are those that allow companies to avoid preventable delays without compromising quality, by optimizing activities. One mantra I endorse is “begin with the end in mind.” Advanced therapy developers need to have a clear understanding of the patient population size they intend to serve with their drug product, the geography in which the drug product will first be marketed, and how their value chain’s nuances may be stressed when scaling up to commercial supply. With this focus, it is possible to create a manufacturing process in early-phase development that is robust enough to address commercial supply.
Activities that may seem peripheral to drug product manufacturing must also be addressed in early-phase work, such as security of supply for critical raw materials. Supply chain management is also important for first-in-human studies; if the advanced therapy is autologous, then scalable chain-of-identity and chain-of-custody strategies should be employed to ensure that modification of supply chain management for pivotal and commercial supply does not delay marketing authorization applications.
Over the last few years, AI and ML have become synonymous with the idea of pushing technological advancements forward and accelerating product development. However, these technologies have not yet delivered on the promise that they would revolutionize drug development timelines and cannot be exclusively relied on to identify druggable targets and/or design novel therapeutics.
Instead, these technologies are best utilized as tools to complement, rather than replace, rational scientific methodologies designed to probe strong, well-defined hypotheses. Companies with sound scientific discipline and robust early data sets typically have well-designed and focused development plans from inception, providing an ideal foundation to leverage retrospective clinical data and predictive software to accelerate product development without sacrificing quality.
Furthermore, optimizing the regulatory strategy and leveraging accelerated approval pathways is an excellent methodology, when applicable, to accelerate product commercialization. The FDA has a number of programs, including Fast-Track, Breakthrough, and Priority Review, that can significantly reduce the time and cost to market.
By combining a disciplined yet resourceful product development plan with indications that are eligible for accelerated regulatory pathways, companies can significantly reduce development risk while cost-effectively accelerating commercialization. This approach maximizes the probability of efficiently translating novel science into groundbreaking therapies that deliver value to both investors and patients.
In response to the growing demand for quicker time-to-market in the biopharmaceutical industry, we employ several strategies to accelerate product development and commercialization while maintaining high quality standards.
One key is leveraging the same technology across different phases of development to ensure smooth scale-up. In our case, that means the availability of a very well-established fixed-bed reactor platform, in which VIVEbiotech has manufactured more than 150 batches in the last eight years under FDA and EMA regulations. This plug-and-play platform allows rapid tech transfer from 2D to reactors and straightforward transfer to large scale, ensuring scalability and reproducibility. Indeed, the same manufacturing platform is being used for developmental, engineering, and GMP batches independent of the scale, which very significantly shortens the required timelines for GMP material availability.
When companies have difficulty finding sufficient capacity at crucial times, it can lead to unnecessary delays. VIVEbiotech has built one of the biggest lentiviral vector programs in the world –– more than 50 batches in reactors per year –– meaning we have capacity to start programs and to allocate new batches slots in a timely manner.
Finally, given the number of new players entering the space, finding a supplier with experience reduces unforeseen delays in a program’s start or execution along the agreed timelines because the appropriate personnel are available from the beginning to give answer to specific projects requirements. This is why we have prioritized stability as we developed our highly skilled and well-trained team, with a very low turnover rate below 5%.
Our industry has applied integrated and transformative methodologies without compromising product quality and safety to accelerate the development timeline from DNA sequence to Investigational New Drug application (IND), which was shortened to 3–6 months. Especially during COVID-19, neutralizing mAbs were developed from DNA to Emergency Use Authorization (EUA) within 14 months, and thousands of kilograms were manufactured within months for therapeutic applications across the globe.
We accomplished this significant improvement by offering a highly vetted and truly one-stop discovery, development, and GMP manufacturing platform. The knowledge we have gained has allowed us to drive timelines down even further when infectious disease concerns require more expedited development.
Strategies for production cell line selection have been continuously improved for greater productivity and efficiency. These recent advances enable us to reshape the CMC strategy so that we can supply clinical materials in as little as three months. Stable pools generated under GMP conditions consistently exhibited similar productivity and product quality at different scales and batches, enabling rapid initiation of phase I clinical trials.
As CMC development timelines have been accelerated over the years, the use of pool-derived materials for toxicology studies has been winning increased acceptance by global regulatory agencies. It is crucial, therefore, to select a final clone with product critical quality attributes (CQAs) similar to the material used for the toxicology studies. Importantly, next-generation sequencing (NGS)-based cDNA is applied instead of peptide mapping by LC-MS to quickly screen for clones without any sequence variants to those pool material cells used in early development. Concurrent cell line stability passage can be done during clone screening to further reduce timelines.
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