January 31, 2023 PAO-01-23-RT-01
The use of induced pluripotent stem (iPS) cell–derived therapies has such potential to expand the types of indications that may be treated via cellular therapy. The utilization of iPS cells as a starting material will require drastic changes to how we think about optimal donors, starting material, and how that material will need to be characterized and qualified for use in the development of a therapeutic cell and/or tissue. It will be critical to gain a better understanding of the critical quality attributes as well as key regulatory requirements (such as genetic screening) to ensure safety and efficacy in patients treated with these truly novel therapies.
The most transformational development in clinical research will be driven by… Wordle. Wordle? Wordle teaches us important lessons that can help transform clinical trials. Consider these:
The power of 5-letter words
In the pharmaceutical industry, we use lots of jargon: “investigational product,” “principal investigator,” “intervention.” This jargon conveys the sense that clinical trials are for scientists, not ordinary people. If we want more people to consider participating in clinical trials, we need to learn to speak to patients using language that is accessible and familiar.
The power of sharing
Wordle became viral because of its shareability. Seeing our friends share results made us curious to try it ourselves. Clinical trial participation, on the other hand, is mostly invisible. Patients agree to participate in clinical research to contribute to society, but then they participate alone, without any connection to the community of participants even in the same trial. We should help participants feel connected to each other and to the higher purpose. We can make clinical trial participation something to be proud of; something to share.
The power of equity
Everyone gets the same Wordle puzzle each day. It’s part of what creates community; we’re all doing it together. Clinical trials shouldn’t only be available to a select few — to patients in big cities with academic medical centers and established research centers. We need to make clinical trials available to the communities that need them, whether urban or rural, majority or minority, in whatever language they speak. Let’s advance medicine for everyone.
Without a doubt, artificial intelligence (AI) and machine learning (ML) are leading the disruption of the pharma advertising industry. AI and ML remove the heavy burden of manually analyzing large data sets and designing campaign roadmaps for marketers and agencies. Also, the rate at which marketing segmentation and ad targeting are transforming appears to be accelerating exponentially with the rise of AI and ML. However, while there are significant benefits, the technology also creates challenges for traditional players in the digital space — the financial investment and the upskilling of legacy team members, to name a few. But the biggest challenge is the opportunity that these technologies provide for nontraditional competitors to now enter the digital space. Historically, publishers have enjoyed a protected universe as content creators. Now, AI and ML, paired with the soaring popularity of content offerings like streaming television programming over a smart TV, means that broadcast advertising can now be targeted to users with the same specificity, if not greater, as any online campaign. Suddenly, publishers have to compete with new adversaries entering the digital space. However, the differentiating factor for these publishers is the audience that their content attracts. If publishers continue to invest in their content strategy, product development, and technology infrastructure, AI and ML will allow them to turn these challenges into opportunity.
The analysis of de-identified patient data sets is critical to the discovery of lifesaving and life-altering therapies for vulnerable populations. It has the potential to provide early warning signs, mitigate the spread of disease, find new cures, and improve/accelerate the accuracy of research and biomedical exploration. Looking ahead, I expect the increased use of analysis of real-time data and the application of predictive analytics to target treatment — specifically, simple diagnostic tools that use artificial intelligence to passively analyze observations and identify patients at risk for developing certain diseases. Moving forward, we need innovative solutions that identify warning signs and allow for earlier intervention and diagnoses.
One of the most transformational technologies will be the inclusion of machine learning and artificial intelligence (AI) embedded into connected, on-line devices that stream process analytics and can be used throughout biopharma development and manufacturing to guide or control decisions and next steps.
Advanced therapies are more complex to manufacture, requiring a multi-fold increase in the number of measurements due to smaller production batches, given the personalized nature of these therapies. To achieve production of the desired product, manufacturers need to identify and understand critical process parameters and their impact on critical quality product attributes during process development and the entire biomanufacturing workflow.
There’s also the need for automated solutions, as scientific and engineering personnel have large workloads that continue to expand. The need for real-time results is also a factor due to the demand to bring therapies to market faster. Automated devices with embedded machine learning / AI that are connected, simple to use, and provide robust analytics will drive significant quality, efficiency, and cost improvements.
The combination of artificial intelligence (AI) and real-world data (RWD) will be most transformative in the near-term. RWD are generated from many different types of sources — essentially everything outside clinical trials — and reflect what's actually happening in real patients' lives, across many dimensions. RWD are drawn from patients with broad ranges of characteristics and heterogenous disease trajectories, care settings, and geographies. This diversity enables the exploration of patterns and outcomes in ways that aren’t possible in clinical trials, or even in traditional registries.
AI encompasses a powerful suite of tools that can provide deeper insight, including into the future, through careful calibration on quality data sets. Once AI models have been optimized, they can generate hypotheses and make accurate predictions on questions with real relevance to patients. AI technology can help identify undiagnosed or misdiagnosed patients, assess likelihood of benefit from a particular treatment, and flag disease progression, flares, or catastrophic events early to improve care management.
AI and RWD are finally reaching the “transformative” stage because both are mature enough to build on each other’s advantages. Well-structured, rich RWD data sets are available and growing across a range of disease areas. AI tools can learn from these data sets and insert predictions exactly where they’re needed — where clinical ambiguity around treatment choice is greatest, for example, or where an undiagnosed patient might otherwise go unnoticed. AI and RWD allow us to gather insights at scale and personalize those insights down to the individual. This is the effect that will prove transformative.
As we look forward to the next phase of the pharmaceutical life cycle, using data analytics in the cloud will have the most transformational impact. Currently, the industry uses machine learning (ML) and artificial intelligence (AI) tools in early drug discovery settings, but with the flow of data enabling greater use cases for AI and ML, the technology is going to make its way all the way down the value chain to the very beginning — even in compliance.
With the move from paper processes to digitally connected science ecosystems, the cloud will help labs and their scientists better explore, discover, and push boundaries of how information is used to produce faster scientific and business outcomes. Cloud access accelerates the ability to connect with other entities, which can help dramatically improve research development and manufacturing excellence, and with it, true quality by design.
Additionally, the collective use of data in the cloud will play a massive role in the years ahead, particularly as the industry looks at how to scale the delivery of new modalities that are highly individualized. From monoclonal antibodies to vaccines, biotech companies are striving to bring safe and effective biotherapeutics to market as fast as possible and will increasingly look to advances in technology including data analytics and cloud capabilities to do this.
How we’ve traditionally recruited patients for clinical trials isn’t sustainable. Fewer than 4% of Americans participate in research. With the proliferation of gene and cell therapies and the FDA’s push to further improve diversity in clinical trials, we need to make clinical research more accessible. As decentralized clinical trials become mainstream and new players like CVS Health and Walgreens launch clinical trial businesses, we are making great strides toward meeting patients where they are. However, we have yet to realize the full potential of real-world data and real-world evidence to accelerate recruitment and boost diversity — this will bring about the greatest transformation in clinical research in the next three years.
Real-world evidence can help us deeply understand the patient’s journey from a clinical and social perspective to assess patterns in diagnosis and treatment or uncover misdiagnosed patients. It can help us select research sites and investigators based on their real-world patient panels and referral patterns, not only based on their past research experience. It can help us better engage physicians treating patients in underserved communities, thus increasing patient touchpoints, trust, and access.
Achieving these benefits requires building the most complete picture of the patient journey by linking together patient-level data such as social determinants of health, clinical, and first-party data. It requires leveraging grouper technology and clinical informatics to build precise cohorts of patients based on a trial’s specific inclusion and exclusion criteria. And it requires understanding provider referral patterns to create community outreach programs based on existing provider networks.
We’ve been talking about artificial intelligence (AI) and machine learning (ML) in our industry for a while now and are starting to see it become more pervasive —in support of traditional use cases, such as automation of manual data activities and anomaly detection, but also for more sophisticated and advanced capabilities like digital twins. I believe digital twins have the potential to be truly game-changing for clinical research. By simulating the control group for a given patient, digital twins could make it possible to generate statistical significance from trials that are smaller, shorter, and faster than we have ever seen before. In the nearer term, it could also improve patients’ relationship with clinical trials by broadening access to treatment rather than placebo. In the longer term, it could revolutionize how we conduct trials altogether. While still in early stages, one of the most critical requirements for enabling digital twin technology is access to ample volumes of data for ML models to pull from. Taking advantage of these ground-breaking advancements around AI and ML, especially something as powerful as digital twins, will require strong technology foundations. I do think it’s going to happen. It will largely depend on strong data infrastructure and analytics foundations that can support the digital clinical data pipelines enabling these disruptive applications of AI/ML.
Has there ever been a more exciting time for the pharmaceutical and healthcare industry? We are living in times of great challenges as a result of supply chain disruptions, regulatory restrictions, and the needed trend towards sustainability. In an already highly innovative industry, these challenges are likely to catalyze even more transformative solutions. Let me talk about just a few examples.
In the oral drug delivery space, we expect to see disruptive technology in oral biologics — oral vaccines, for example, provide social and economic advances, especially when we look at developing countries. In terms of parenteral drug delivery, we see future nucleic acid therapeutics as the most disruptive technology on the horizon. This is especially exciting when nucleic acid therapeutics are combined with individualized medicine concepts, such as in antitumor vaccination. New RNA modalities will also advance new therapeutic concepts.
Exosomes –– lipid-containing membranes and membrane proteins — could transform (bio)therapeutics because they have the potential to lead the way in exploiting inherent natural mechanisms to treat, diagnose, and prevent disease. Disruptive technology could also come in the form of quantum computing in pharma, and in the long-term cellular reprogramming to prevent disease by changing the very nature of the cell itself. AI also has a truly transformative potential once it becomes more mature. It will disrupt all aspects from drug discovery to prescribing the final medicine for patients. Finally, individualized medicine that targets an individual’s genome — which could be made possible by micro-manufacturing — has the potential to upend healthcare as we know it.
The continued innovation and adoption of digitalization is transforming biomanufacturing and, in doing so, helping to bring new curative treatments to the forefront. Digitalization has a significant impact on how therapies are brought to market, delivering them to patients faster and more cost-effectively than ever before.
From a process development perspective, we see a continued drive to miniaturize and automate operations. One of the advantages of this trend is that it increases the amount of data generated at the small scale and often also simplifies the digital connection of the data generated at the process development scale to the data generated at the GMP scale. This is aligned with regulatory guidance around the need for process knowledge and data integrity.
This also ties into the increased implementation of process analytical technology (PAT), a regulatory framework that encourages innovation in pharmaceutical development, manufacturing, and quality assurance. PAT aims to build quality into biopharmaceutical production processes by monitoring and controlling the process in real time. PAT combined with process controls results in increased product quality and reduced out-of-specification and out-of-trend events while facilitating a dynamic manufacturing process that can compensate for variability in raw materials and equipment.
The needs and challenges ahead of the pharma industry will continue to progress, and utilization of PAT and these technologies will enable the industry to drive better efficiencies, speed, and costs for the benefit of patients.
Across the industry, we see more development of precision medicine based on pharmacogenetics, such as mRNA vaccines for cancer immunotherapy and a growing need for access to gene therapies.
mRNA vaccines for cancer immunotherapy
In cancer immunotherapy, the most advanced application of mRNA is therapeutic vaccination, which leverages both the capability of mRNA to deliver genetic information and its innate immunostimulatory activity. The latter is particularly important for breaking immune tolerance when cancer-associated self-antigens are targeted.
Unlike prophylactic vaccines, mRNA vaccines for cancer immunotherapy need to be produced at smaller scale and to be tailored to the tumor antigen to be efficient. This makes the cost per dose higher than prophylactic medicine, which means that improving process economics here is critical.
The mRNA vaccines are tailored manufactured based on the specific molecular features of their tumors. It takes 1–2 months to produce a personalized mRNA cancer vaccine after tissue samples have been collected from a patient. Speed is especially important for individualized cancer vaccination.
Efficient delivery of these vaccines is paramount. Lipid nanoparticles (LNPs) are currently the favored delivery technique for mRNA, but they still need to be optimized for more efficiency, better targeting, and increased vaccine stability. Other types of non-viral delivery technologies may emerge in the next few years to overcome the LNP caveats and be more suited for cancer immunotherapy.
Increased access to gene therapies
The number of approved gene therapies is increasing. With the first FDA approval for a gene therapy for hemophilia B in November 2022 – Hemgenix – the door is now open for gene therapies for genetic disorder that are treatable. While SMA and hemophilia B still have low prevalence (1 birth/10,000 for SMA and 1/25,000 male births), we can soon foresee gene therapies for genetic disorders with higher prevalence, such as hemophilia A (1/5,000 male births).
Right now, these treatments are too costly (over $2M for Zolgensma and $3.5M for Hemgenix) to be sustainable for diseases with higher prevalence, and this comes down to the inefficacy of the manufacturing process. Reducing the cost per dose of gene therapy treatments by increasing process efficiency is the barrier that must be overcome for true CGT commercialization.
The economics of the process includes cost and time and must also consider quality and reproducibility. Working with suppliers that understand the full scope of drug development and commercialization challenges and provide reliable, scalable solutions tailored to each unique process is critical to success. Polyplus has been working for several years on how to improve upstream process economics by providing reagents and services that allow higher viral titers, while working in a GMP-compliant environment.
Every sector of the pharmaceutical industry, from research and development to manufacturing QC, is looking for additional value from the vast amount of data it produces. The experiments that are conducted in labs, the information collected throughout clinical trials, and the investigations undertaken for drug safety have obvious immediate value. The question everyone is trying to answer is “How do we enable data science to deliver significant additional value from this ocean of data?”
For example, those working in drug development may want assistance identifying the “sweet spot” for relative proportions of ingredients within a tablet formulation. Intelligent mining of historic experimental records promises to deliver new formulations with fewer formulation candidates and significantly fewer lab-based experiments. It doesn’t take much imagination to see multiple opportunities to apply such approaches within life science workflows or to recognize the cumulative effects this could have on time to market.
The holy grail is “How can we optimize our processes?” and “Can we achieve more by doing fewer experiments done ‘smarter’?” The first step to achieving this is to connect data that is created across an organization — from the labs, through research and development, to clinical, and beyond. But how do we go about such a task? There is no one solution to this challenge, as no vendor at this stage can offer a single solution that can provide connectivity to this extent. There exists a range of different systems which can offer elements, but not one end-to-end solution which allows people to extract the data they need, irrespective of where they work.
Gall’s law states that “A complex system that works is invariably found to have evolved from a simple system that worked,” and I think this is particularly suited to building this new paradigm. Due to the complexities involved, we will be well served by thinking big and starting small. The cracking of this challenge will be the single most transformational technology in use in pharma, enabling companies to mine their existing IP better and more efficiently.
The question of “Which emerging biologic will replace the vast success of monoclonal antibody?” has been consistently debated within the biopharma industry since the early 2010s. Given that mAbs yet take a large share of the biologics market, no definite answer has been formed around the question. But it is important to note that more types of modalities, such as ADCs, bsAbs, AAVs, and mRNA have surfaced on the market and that their complexities continue to increase. This new wave has induced new technological advancements and allowed for new ways to reach the drug targets unlike any before. Statistics, AI, and various IT technologies enable the complexities involved in various stages of drug development (target discovery, drug design/screening, MoA evaluation, CMC development, manufacturing process analytics and monitoring, clinical evaluation, etc.) to be dealt with in a much more efficient manner. However, as the degree of complexity of biology is high, and there are yet a slew of questions left undefined, many efforts to employ the new technologies into drug development still remain in early stages. Nevertheless, I believe that a key to resolving complexities in development will soon be found as technologies continue to evolve especially in drug design or MoA target screening for new biologics development. Meanwhile, I expect the United States and Europe will continue to lead the global pharma market for the foreseeable future.
In viral vector manufacturing for cell and gene therapy, the market will see a lot of new technologies and developments emerging in the years ahead with the evolving regulatory environment and challenges to meet economies of scale as the main drivers.
On the processing side, transformational technologies will take place within both upstream and downstream steps, for example:
I think that more automated methods will be developed in analytical methods: high-throughput techniques to reduce time-to-development and time-to-release and certainly more PATs (process analytical technologies) with specific and cell-friendly sensors near biotanks, which are already ongoing and will be increased by the market.
But in general, the main transformation will also be cultural and organizational: CGT personnel still mainly come from academia or R&D cultures and have a high level of expertise level but lack an industrial and commercial mindset. Techniques such as the “data-driven approach,” “risk-based approach,” “digitalization,” and “Lean manufacturing” will contribute to improving quality, security, and industrial performance. The transformation of CGT organizations will be led by these methodologies and certainly will induce new technological innovations.
Personalized medicine, in which therapies are customized for patients, moves away from blockbuster, one-size-fits-all drugs and continues to be the industry’s most disruptive development –– and it’s being driven by a complementary set of digital technologies that are changing the way these therapeutics are discovered and made.
Assisted by big data and fueled by artificial intelligence (AI), considerable new investments have been made in the digital technologies being applied to creating therapeutics for oncology, autoimmune disorders, and neurological diseases. AI and digitalization are being used in two ways: for lead generation and to facilitate automation of the manufacturing process.
Lead discovery starts with an understanding of the cause of disease, followed by modeling treatments leveraging AI and high-throughput data. Using digital tools avoids having to generate and test millions of candidates in the lab, and it saves a tremendous amount of time. Automation will go a long way to expediting manufacturing by linking diagnostic AI directly to digitalization of the manufacturing process (e.g., high-throughput automated systems in development for making mRNA and pDNA). Starting with the genetic information to make a therapeutic work for a specific patient, new therapies can be manufactured with the same efficient process simply by altering the genetic sequence. And, because personalized medicine only requires the synthesis of nanograms or micrograms of therapeutics, the equipment and facility have a much smaller footprint. Automation will be disruptive because it optimizes the process, reduces some workforce requirements, and drives down the cost of manufacturing.
In the current age of fast-paced technological advancement, artificial intelligence (AI) is transforming the way scientific labs are run. I see three major ways AI technology is predicted to disrupt the lab industry in the coming years: increasing lab efficiency, supporting diagnoses and decision making, and minimizing costs and human error.
Many new AI technologies have the capability to predict a system failure weeks before one occurs. Using machine learning, AI can monitor predictive maintenance data, inventory depletion rates, and supply chain information to automatically and efficiently schedule instrument repairs and deliveries of replacement parts.
In terms of day-to-day lab activities, this technology can reduce manual handling (for example, tube sorting and sample handling), giving employees time to focus on data analysis. In supporting diagnoses, AI algorithms can be trained to quickly and accurately classify cellular objects using a database of specimen images. These findings are then compared with specimens from across demographics, various health systems, and locations to identify problems and diagnose patients. And as more data are transferred through AI systems in the coming years, this technology will only become more accurate.
In addition to helping make better clinical decisions, AI’s ability to sort through data can also save money and reduce investigative inaccuracies. Taking the human element out of menial analytical tasks eliminates unnecessary instances of human error and creates a highly accurate laboratory system.
I believe that the most disruptive and transformational technology on the horizon is the application of chemical biology to therapeutics. There have been amazing developments on this front, such as protein degraders, protein stabilizers, and other technologies that take drugs beyond the idea of inhibiting the activity of a single protein to achieve a clinical benefit. Recent FDA approvals for drugs treating KRAS-mutant cancers highlight the opportunities to develop therapies for targets previously considered undruggable.
Click chemistry is a Nobel Prize–winning concept, where two compounds “click” together with each other but ignore everything around them. When applied in living systems, it allows for the localization of a therapeutic at the place in the body where it is needed. This technology is the backbone of a new platform Click Activated Protodrugs Against Cancer (CAPAC™), designed to minimize the toxicity and dramatically improve the efficacy of cancer therapeutics.
Shasqi is the first and only company in the world to use click chemistry in human patients. Our early clinical data suggests that a high dose of cancer therapies can be localized directly to the tumor over a prolonged period of time, at unprecedented amounts and without dose-limiting toxicities, unlocking new biological effects such as enhanced immune activation at the tumor site. We believe click chemistry can transform how we treat cancer by creating therapies that have much greater safety and efficacy than we have today.
I believe that technology is the strategic issue of our time. I have been part of fast-growing companies having one thing in common — they harness technology and innovation to their advantage. This has required boards and executive teams to go beyond using technology for the core of their business to actively building new businesses, business models, products, and services, using powerful, rapidly maturing technologies and new skills.
The most transformational changes across the business, driven by digital transformation, include: business process transformation, operating model transformation, and cultural transformation.
The most transformational technologies that will have a direct impact to business value creation in the next 3 years will include: hyperautomation, predictive analytics, cybersecurity, and cloud.
The transformational technologies that are in an early stage of development but will accelerate fast and create business value in the next years include: blockchain, artificial intelligence (AI) / machine learning (ML), and virtual reality / augmented reality.
In the last 20 years, we’ve seen huge improvements in the diagnosis of rare diseases, but it is still a daily challenge that needs specialized expertise. The current total cost for rare disease diagnosis is estimated to be about one trillion dollars, with many families burdened by a long diagnostic odyssey that often relies on trial and error to find out what is wrong with their sick child. Untargeted metabolomics, utilizing mass spectrometry platforms as first-line diagnostics for rare diseases, is now making its way into clinical practice.
At Metabolon, we believe that the use of our individualized diagnosis of rare diseases will be the most disruptive to the industry. We can currently diagnose more than 100 rare diseases, specifically in cases that lack a clear genetic basis underlying the disease-specific clinical phenotype. Using our proprietary technology, we can provide an immediate clinically translatable solution for treatment to improve patient outcomes worldwide. Our success to date supports the belief that our platform can significantly improve diagnosis to several hundred more patients annually, all from only 250 microliters of blood (a drop of blood). The use of metabolomics has the potential to revolutionize the rare disease diagnosis and newborn screening markets and is especially appealing since it is complementary to genomics and, when used together, has been shown to greatly increase disease diagnosis rates.
With the rapid expansion of cell and gene therapy programs, pressure is on biotherapeutic manufacturers to increase productivity. While chromatography is the workhorse for the purification of biotherapeutics, it is resource intensive and can cause bottlenecks due to low throughput. In particular, the capture step can be a significant contributor to product loss in downstream processing. This is particularly relevant for new therapeutic targets. Traditional diffusive media is optimized for the capture and polishing of proteins. Vector targets are often 10 larger, so they cannot access the interior binding surface of the pores in traditional porous media, leading to restricted capacity.
A step change is required to go beyond the limitation of traditional chromatography media, and electrospun nanofiber membranes have the potential to address these key challenges. The membrane structure is porous, allowing almost immediate access to binding surfaces, thus negating the need for lengthy residence times. The nanofibers provide an increased surface area-to-volume ratio, enabling increases in capacity. Void volumes are also reduced, leading to reduced buffer usage and associated generation of waste.
Astrea Bioseparations has developed a unique composite electrospun nanofiber membrane, AstreAdept, with very high capacity that is sustained under very high flow rates. Consequently, processing times are reduced, and yields are increased compared with traditional chromatography media. By compressing downstream processing steps, such as capture and polishing, the potential of this novel technology to transform bioprocessing economics is highly significant. A more efficient downstream processing would shorten development and manufacturing timelines, enabling treatments to reach patients sooner.
Advancements in cell and gene therapies will most likely be the greatest disruptive and transformational developments in the biopharma industry. Novel cell therapies will deliver more precise targeting, be better matched to individual patients, and address harder to control diseases, such as solid tumors. Novel gene therapies will be able to edit DNA or modulate RNA, leading to treatments for previously uncurable genetic disorders. Both cell and gene therapies will require breakthroughs in drug delivery technologies that are required to precisely and safely deliver these novel therapies to the target organ or tissue.
As regulators and payers look for new information to guide their decision-making, real-world data and evidence are poised to play a transformative role in not only getting drugs to market quickly but also ensuring that patients can access them.
This RWD-driven disruption can and should happen at every stage of the drug life cycle. RWD is a powerful mechanism for action during the R&D and clinical phases, where it can be used to identify unmet needs and prioritize product portfolios, reduce investment risk, improve clinical trial design and recruitment, and more. And during the commercial stages, RWD is a tool for helping inform payer decision-making or making post-launch business decisions, such as supporting a label extension.
Lastly, with the proliferation of these new data sources comes the need for the right technology to help synthesize and glean important insights.
My response may somewhat surprise you. I believe that the most transformational technology for life sciences and biomedical research is an unfreezing of legacy clinical research operating models, technologies, and embedded beliefs. Intelligent digital technologies that are integral to research site workflows and those that capture study data with no manual entry will become the mainstay of all pharma, biopharma, and CROs in the coming three to five years. The pandemic forced use of decentralized trial and other digital solutions, created more openness for data exchange and collaboration, and moved to scale technologies that were nascent and “PoC scale” pre-pandemic. This was the crucible that showed what could work and not work; what could handle a study and large-scale sites and what couldn’t; and what delivered sufficient value to replace older working approaches. But it was the absolute acceptance of the inevitability of these solutions that really created the disruption and transformation.
In the coming few years, all translational sciences will use multimodal data and AI solutions to define and validate drug targets. AI SaaS solutions will be used to design and optimize all clinical trials and post-approval studies. And intelligent digital trial solutions will move from running 5–10% of studies to doing 50–70% across settings and geographies.
The most disruptive technologies are technologies that actually work. Just to mention a few: CAR-T, cell therapy, gene therapy, bispecific antibodies, DNA vaccines, mRNA, and antibody–drug conjugates are among mention some of my personal favorites. Then worth mentioning would be different gene delivery vehicles like lipid nanoparticles in combination with DNA, RNA, transposons, or CRISPR/Cas9, to mention a few different examples. However, the gene delivery modality of today and for at least a few more years is AAV, with ~800+ projects in the discovery and preclinical phase. The beautiful nature of AAV is that is less immunogenic than other viral vectors, forming concatemers, and therefore is quite stabile in vivo, can be manufactured in Biosafety Level 1, and can be designed with different tissue tropisms. It is by far my favorite in gene delivery modalities.
The AAV (adeno-associated virus) gene therapy CDMO market should have a strong growth over the 2022–2027 period, with numbers reaching 20–30% CAGR, backed by strong financings, a unique therapeutic potential, health agency support, technological progress, and lack of capabilities, leading to both rising pricing and outsourcing rates. Collaboration with CDMOs allows both pharma and biotech companies to convert fixed expenses into variable costs, limit CAPEX investments, and access specialized technologies, such as AAV manufacturing expertise, while keeping flexibility.
The viral vector market is expected to grow more constrained, as the CAPEX into new GMP drug substance suites cannot keep pace with high demand from growing development pipelines. This capacity constraint will drive the time it takes between an order and initiation of batch manufacturing up from 1–1.5 years today to 1.5–2 years in 2027, consequently also putting pressure on increasing the price per batch. Therefore, the AAV gene therapy market is foreseen to be one of the most attractive markets in the pharma industry, for the next 5 years and beyond.
Our rapidly growing global health crisis is primarily driven by complex, multifactorial diseases, such as obesity and diabetes. The revelation that metabolic diseases associate with deficient nutrient sensing and enteroendocrine signaling has fueled interventions that largely focus on replacing individual enteric hormones involved in glucose metabolism and appetite. While these approaches have provided patient benefit, these interventions are necessarily inefficient, since enteroendocrine signaling incorporates a complex network of cells and hormones that work in concert with highly coordinated spatial and temporal dynamics. It is therefore not possible to restore deficient enteroendocrine signaling with individual hormones that are delivered systemically with imprecise timing and at high concentrations. Furthermore, the high hormone doses utilized potentially cause negative side effects, which may limit treatment eligibility and long-term use.
New technologies that leverage the full complexity of endogenous signaling mechanisms represent a disruptive event in the fight against metabolic diseases, as they aim to provide a safer and more efficacious means to improve patient outcomes, particularly when naturally occurring nutrients can be deployed. These new approaches capitalize on innovative drug designs that deliver nutrients to specific regions of the small intestine, thereby restoring underutilized endocrine, neuroendocrine, and neuronal signaling mechanisms that influence metabolism (e.g., glucose, lipids) and behavior (e.g., appetite, hunger, satiety, food consumption). Preliminary results from initial trials are encouraging: they have delivered proof of concept and demonstrated a superior safety profile. Thus, there is optimism that these technologies may help to turn the global fight against obesity and diabetes in our favor.
A technology that can transform the field of cancer and diagnostics is one that can detect the precursors of cancer, before it has developed, to enable cancer prevention and do so in a patient-friendly way to ensure patient screening compliance. An example is screening for colorectal cancer (CRC), the second most common cause of cancer death in the United States. Colonoscopy is the gold standard to screen for precancerous polyps, and –– while screening rates have improved over the years –– about a third of the average risk-screening population does not undergo the procedure in the United States, missing an opportunity for potential cancer prevention. A major barrier is the requirement for bowel preparation and the invasiveness of the procedure.
A number of noninvasive tests are available or in development, but most are designed to detect cancer, not pre-cancerous polyps, or still require bowel preparation. A disruptive solution will be such that removes the preparation aspect plus the invasiveness, so that it is more convenient for patients and still can detect precancerous polyps with confidence. That is our mission at Check-Cap, and we are excited to be advancing our capsule-based technology through a pivotal trial in the United States to potentially offer patients with an alternative solution in the coming years.
I believe that the most disruptive technology on the horizon is the use of computational disease models and AI platforms for drug discovery and development. The advanced insights from these technologies serve as a catalyst for the discovery of new mechanisms of action and the development of next-generation therapies across all disease indications for biotech and pharmaceutical companies. Health tech companies like CytoReason are leading the way in this transformational shift in the industry to digest large data sets into actionable insights that save biopharma companies money, time, and resources as they work to bring their drug to market for patients in need. Through the use of AI and ML, scientists are able to map and compare treatments, patient groups, and disease mechanisms by utilizing computational disease models that are backed by top-tier publications with the latest genomics data and cell-focused natural language processing. The data in the models is broken down by each disease indication, tissue type, patient population, and disease severity. The insights give scientists a bird’s-eye view of entire drug portfolios and help them make informed decisions that can help their biopharma companies prioritize new targets, find biomarkers, and predict which patients may best respond to novel treatments.
I continue to be inspired and humbled by the scientific boundaries that companies are pushing in order to develop new therapies. An area of innovation that I believe has untapped potential and from which we will see disruptive therapies emerge in the coming years is epigenetics. Rather than tackling specific pathways or genes associated with disease, epigenetic therapies work to modify the expression of multiple genes involved in relevant cellular functions and which may be dysregulated in many diseases. While a few epigenetic therapies have been approved in the area of oncology, these have mainly focused on destroying dysfunctional cancer cells. The new wave of epigenetic therapeutic development aims to leverage epigenetic modulation to restore cells to a healthy state. From small endogenous epigenetic molecules to more complex technologies that enable epigenetic reprogramming, we look forward to seeing more epigenetic programs in development for multiple diseases, such as metabolic, regenerative medicine, and immunology, among others. Some of these are already showing potential to provide clinically meaningful results in diseases that currently lack effective treatments, including alcohol-associated hepatitis, an indication we are exploring with our lead candidate in development. I am excited to see what the field will bring in the near and long-term.
I’ve seen two areas within oncology in particular that have grown significantly with revolutionary potential in the coming year: therapeutics targeting KRAS mutations and the re-emergence of cancer vaccines.
Mutated KRAS has long been thought of as an “undruggable” target due to its smooth surface, which makes it challenging for potential therapeutics to bind to it. In addition, there are several KRAS mutations, so even if a therapeutic is able to successfully target one or two, drug resistance may emerge due to other mutations that the drug isn’t effective against. There is ongoing work to try to address some of these roadblocks. Parallel to this has been the resurgence of cancer vaccines, which have historically faced several issues, including proper antigen selection, poor trafficking to the lymph node, and overcoming the immunosuppressive tumor microenvironment.
At Elicio, we’re working to address some of these challenges with our lead asset, ELI-002, a therapeutic cancer vaccine that is being studied in mKRAS-driven tumors. The vaccine has been designed with our lymph node–-targeting Amphiphile technology, which allows ELI-002 to “educate” T cells to recognize KRAS G12R and G12D mutated cells and lead to their elimination. We also have plans to expand the targeting spectrum to the seven most common KRAS mutations, which provides a potential solution for the ongoing challenge of drug resistance.
We look forward to seeing how this space will progress in the year ahead and the role Elicio could play in giving patients with KRAS mutations a fighting chance.
The RNA therapeutics field continues to have transformational potential, with the global RNA-based therapeutics market projected to reach more than $25 billion by 2030. The development of successful RNA-based COVID-19 vaccines has demonstrated that RNA is a powerful tool whose potential should be explored further to design not only additional vaccines but also other kinds of therapeutic agents.
RNA therapeutics have a number of advantages, including the ability to act on historically “undruggable” targets, cost-effective development, efficient manufacturing, and the possibility to readily alter constructs as needed for a variety of purposes. On a related note, RNA is versatile both in structure and function. It comes in several different forms (e.g., mRNA, siRNA, miRNA, aptamer (RNA)). Each of these subtypes has its own individual benefits with potential to create vaccines, cell therapies, gene therapies, and other modalities. RNA therapeutics are currently being studied in clinical trials for a broad spectrum of diseases, including a variety of tumors, infectious diseases, and rare diseases.
At Elixirgen Therapeutics, we’re excited to be a part of the rapidly evolving RNA field with our proprietary controllable self-replicating RNA (c-srRNA) platform. Our technology has potential advantages in durability, controlling gene expression, and manufacturing. We are currently studying our autologous cell therapy EXG-34217 in telomere biology disorders, a rare disease caused by shortened telomeres. We look forward to continuing to be a part of expanding the potential of RNA therapies to meet the needs of patients globally.
Digital medicine tools continue to gain traction and popularity among healthcare providers, patients, and the biopharma industry. In order to find what are likely to be the most transformational developments in digital medicine, we look to the fields of psychiatry and the treatment of brain health disorders like anxiety and depression.
Measurement of disease symptoms with digital tools can confirm and elucidate a reality about the world that most practitioners already know: symptom-based diagnoses of systemic illnesses necessarily dimensionally reduce complex, disparate biological pathogenic cases into ontological entities that are not unitary. Drug development and treatment delivery in this diagnostic paradigm lead to suboptimal outcomes for drugs under development and limited options for providers, leaving many with residual suffering.
Advances in precision psychiatry may help improve personalized treatment options by illuminating how therapies impact individuals differently. Emerging digital medicine tools may help providers support patients throughout and beyond the treatment cycle while collecting and analyzing data to facilitate payer adoption of promising therapies. These tools may prove especially helpful in advancing novel, consciousness-altering treatments like psychedelic-assisted therapy, where the patient–provider dialogue may be inhibited, and reliable, real-time data feedback is essential for patient safety.
Drug development will continue to play a vital role in advancing the next generation of treatments for brain health disorders. Improving the drug development process and, thereby, patient outcomes require a finer-grained phenomenology driving a deeper understanding of underlying disorders, ultimately informing how medicines of all classes may be most effectively deployed as treatments.
Almost all diseases, regardless of etiology, are caused by changes in gene expression. Despite significant advancements in genetic medicines, we have so far not been able to treat disease at its source using the same approach that Nature does to control gene regulation and cellular programming: epigenetics. By leveraging this universal operating system, an approach called epigenomic programming, we could precisely control gene expression and cellular physiology to ultimately treat or cure almost any disease.
However, understanding epigenetics is not as simple as decoding the genome. Multiple diverse mechanisms work in concert to regulate gene expression. Different types of epigenetic marks are made at particular points in the genome of each cell to turn genes on and off without ever changing the underlying sequence of the DNA. The ability to utilize the full range of epigenetic modifications is key to developing epigenomic medicines that can tune expression of specific genes in a controllable way. Only now, through major advances in scientific understanding and computational techniques, are we able to describe how this system of epigenomic programming works and, more importantly, how we can coopt it to treat disease.
From regulating oncogenes long considered undruggable to selectively activating silenced genes to enable tissue regeneration, epigenomic programming offers unprecedented opportunities for designing therapeutics to treat the underlying cause of serious diseases. At Omega Therapeutics, we are harnessing the power of epigenetics and mRNA as a programmable therapeutic modality to develop an entirely new paradigm to control gene expression and treat disease.
The World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) have both recognized antimicrobial resistance (AMR) as an imminent threat to human health. Recent studies have shown that the pandemic has exacerbated AMR infections due to ESKAPE pathogens, a well-recognized group of bacteria with multidrug-resistant properties that render existing antibiotics ineffective. In 2019, ESKAPE pathogens were responsible for 42.2% of blood infections (around 50 million) each year, resulting in one in three deaths in hospitals throughout the United States. By 2050, it is estimated that AMR-related deaths could increase to as many as 10 million globally. A novel treatment in the AMR space would be revolutionary, given the threat that dangerous pathogens pose. With no new class of antibiotic in over 30 years, a novel class of medication that can tackle multidrug-resistant bacteria and does not increase antibiotic resistance would be able to tackle the constant infective threat.
At Recce, we have developed a synthetic anti-infective compound, RECCE® 327 (R327), that has demonstrated bactericidal activity against all six ESKAPE pathogens without contributing to resistance. With a unique mechanism of action, the compound has the ability to continuously kill multidrug-resistant superbugs. Phase I clinical trial results have demonstrated that the drug is safe and well-tolerated in healthy subjects, and phase II trials are progressing to assess the efficacy of R327 for multiple indications, including diabetic foot ulcers and urinary tract infections. Looking ahead, a solution must be found to address antibiotic-resistant superbugs and emerging viral pathogens.
Disruptive developments will be therapeutics that can provide superior long-term disease remission with less frequent dosing and without constant immune suppression for patients with chronic disorders, such as allergic or autoimmune diseases.
Existing therapies for the millions of patients worldwide living with these diseases often only provide short-term disease remission in a limited number of patients. In addition, current therapies suppress the immune system putting patients at risk of developing serious infections, cancers, and other life-threatening side effects.
Most therapies target the inflammatory process characteristic of these diseases once it has occurred. Therapies that reset the immune system “upstream” of the inflammatory cascade, to avoid inflammation without suppressing the immune system, have the potential to revolutionize the treatment landscape for these patients. These therapies work to restore immune balance by increasing the number of regulatory T (Treg) cells, which prevent the immune system from entering overdrive and attacking the patient’s own body. At Revolo, we are excited to be leading this innovation with two drug candidates, currently under evaluation in multiple clinical trials across indications, such as allergic disease, eosinophilic esophagitis, and rheumatoid arthritis.
The genomic revolution has opened the floodgates for therapeutic development in genetic medicine. However, it has been largely limited by the vectors that deliver them. For example, some current viral vectors lack tropism to the central nervous system, and others require redosing even at low to moderate doses, such as in hemophilia. Although viral vectors are masters at delivering their cargo to target cells, those currently in use elicit a robust immune response that prevent redosing. Ring took an entirely new approach and looked to harness the power of harmless viruses naturally resident in humans, also known as commensal viruses, to address this major limitation. Development of a novel vector based on commensal viruses may redefine what is possible in genetic medicine today by offering a viral vector that does not elicit an immune response yet effectively delivers genetic cargo to specific tissues without genome integration. Anelloviruses are the most abundant commensal viruses in humans, and Ring has harnessed the unique biology of this family of viruses to build a revolutionary new platform and an entirely new class of programmable medicines. Anelloviruses are more than 5 as diverse as current vectors, persistent, and redosable. Through the development of Ring’s Anellogy™ platform, natural anellovirus characteristics can be identified and harnessed to build highly tropic and immune-favorable AnelloVector™ therapeutics. Supported by a foundation of in-house scientific discoveries and publications, this technology has broad applicability across diseases and holds tremendous promise for unlocking the full potential of the field of genetic medicines.
The prevalence of autoimmune diseases is increasing worldwide, yet the current standard of care has hardly evolved to treat immunosuppression, which is associated with undesirable side effects and leaves patients vulnerable to serious infection and malignancies. Restoring natural self-tolerance and avoiding the need for chronic and systemic immune suppression would be truly transformational. At Selecta, we are aiming to accomplish this through the administration of our ImmTOR® precision immune tolerance platform with nanoparticle-encapsulated self-antigens. When administered, ImmTOR® nanoparticles are filtered by the lymph nodes, spleen, and liver, where they are taken up by antigen-presenting cells (APC), such as dendritic cells (DC), a type of APC that can trigger immunogenic as well as tolerogenic responses from T cells. ImmTOR® promotes the induction of tolerogenic DCs, which go on to activate antigen-specific regulatory T (Treg) cells to inhibit the immune response to the antigen. When ImmTOR® is combined with IL-2 to make ImmTOR-IL™, the two have been observed to work synergistically to increase the magnitude and durability of total antigen-specific Treg expansion. Autoimmune diseases with single, well defined, auto-antigens will serve as the proof of concept for ImmTOR-IL™. An example would be primary biliary cholangitis (PBC), a T cell–mediated liver disease driven by PDC-E2. Through re-imagining immunotherapy for autoimmune diseases, Selecta’s ImmTOR® platform is poised to disrupt the therapeutic landscape for autoimmune disease and improve the lives of patients suffering from these serious and debilitating diseases.
AI and machine learning are transforming the way we discover new medicines. Coupled with recent advancements in biological characterization — proteomics, transcriptomics, metabolomics — we can now characterize diseases at a higher resolution than ever before. We currently have the opportunity to move away from the constraints of the past, such as focusing drug discovery on a single target cell, and create therapeutics that treat disease more holistically at the level of the tissue microenvironment.
Although diseases are often described as dysfunctions of a single protein or cell, in reality, they often are defined by tissue microenvironments — networks of cells and signals that work in harmony in healthy tissue but are dysregulated in disease. With machine learning, we can analyze the millions of data points generated by ‘omics research to understand the rules of how cells and signals interact in a specific disease’s tissue microenvironment. These insights allow us to take an entirely new approach to creating novel medicines.
Traditional drug discovery typically selects a cellular target, tests a series of molecules against a limited number of phenotypic assays, identifies a patient population, and then sometimes characterizes the drug’s effects on nearby disease-relevant cells in a limited way. Machine learning allows us to invert this process. We can now start the drug development process with the end in mind and derive a clear understanding of the microenvironment changes required to induce disease resolution upfront at a much larger scale. Instead of taking a trial-and-error drug discovery approach, we can now generate therapeutics in a programmable manner, significantly improving success rates.
The therapeutics of the future will be designed to holistically affect all the cells and signals in a disease’s tissue microenvironment, providing a revolutionary path to generating novel, life-altering medicines for patients across a broad range of diseases.
The development of CAR-T cell therapy as a viable cancer treatment option has been transformational for patients with hematological malignancies. Yet, despite some remarkable results in liquid cancers, we have yet to translate that success to the challenge of treating solid tumors. To date, the most significant obstacle has been the suppressive tumor microenvironment. The most disruptive technologies on the horizon will be those that are able to expand on the successes of CAR-T cell therapy by enabling it to overcome the defenses of the tumor microenvironment and penetrate solid tumors. Since 90% of adult cancer patients have solid tumor malignancies, a successful solid tumor cell therapy technology would have a major impact on the field of immunotherapy and oncology overall.
At SOTIO Biotech, we are working hard to do just this; our efforts involve improving the fitness of CAR-T cells with a GOT2 transgene that can be co-expressed with tumor-targeting receptors to overcome tumor resistance and improve the function of T cells in the solid tumor microenvironment. Known as the BOXR cell therapy platform, our approach is designed to improve the functionality of engineered T cell biology that regulates pathways essential for cell growth, proliferation, and survival under various suppressive conditions. SOTIO’s lead CAR-T cell therapy candidate, BOXR1030, has demonstrated the ability to overcome the harsh microenvironment and the multiple mechanisms of immunosuppression that exist in solid tumors models in preclinical studies, signaling its potential for effective treatment in humans. We are taking these promising preclinical results into the clinic with our first-in-human, phase I/II DUET-01 trial of BOXR1030 in patients with glypican-3 positive (GPC3+) advanced solid tumors.
The future development and advancements of CAR-T from not only the BOXR program but the industry at large will be instrumental as we aim to tackle some of the most difficult to treat cancers in the world.
Despite significant scientific advancements, many cancer therapies are only effective for a fraction of patients and often for unknown reasons. Targeting the expression levels of oncogenes instead of focusing solely on their mutational status is a disruptive approach to the oncology space. Ninety-eight percent of the genome is regulatory and non-coding and may hold those answers. Regulatory regions of the genome control the expression of genes fundamental to the function of all cells, and alterations of these regulatory regions can contribute to oncogenesis. For this reason, regulatory regions likely hold a vast number of potential new therapeutic targets for controlling gene expression, and tapping into this treasure trove has only just begun.
One such example is retinoic acid receptor alpha (RARα), which is overexpressed in about 30% of patients with acute myeloid leukemia (AML) and 50% of patients with myelodysplastic syndrome (MDS), where current therapeutic options are not always effective. Our lead candidate, tamibarotene, is an oral RARA agonist that binds to transcription factors in these regulatory regions to directly target RARA overexpression. Tamibarotene is currently being evaluated in two clinical trials and has been shown to have robust clinical activity, with a tolerable safety profile, in combination with azacitidine. We believe that controlling overexpression of genes across diseases, such as cancer, can be truly transformational to patients that are unresponsive to current options, and this expression can make a world of difference.
There is a need for novel technologies to optimize development and distribution of biologic-based treatments, such as cell and gene therapies. Biologics as a modality have been growing in demand but are highly complex and are generally unstable at room temperature, which makes them sensitive and expensive to process, store, and distribute. Typically, these therapies require specialty manufacturing, distribution, and warehousing, which can hinder how quickly and easily new biologic products are developed and deployed.
Developing dry powder formulations of biologics, compared with their more traditional liquid form, is one way that some of these handling and storage limitations can be addressed. Dry powder formulations can then be administered directly via oral inhalation or intranasal administration, which may have additional efficacy benefits, or they can also simply be reconstituted as needed before administration via more traditional delivery routes.
Innovative technologies in drug development can revolutionize applications in the industry and, most importantly, improve patient care. While conventional freeze-drying techniques are effective at converting some modalities to dry powder, they do not enable a directly respirable dry powder. At TFF Pharmaceuticals, a novel Thin Film Freezing technology, for example, is being leveraged to transform APIs into stable dry powders that are inherently respirable and are suitable for self-administration via intranasal and oral inhalation routes. TFF powders can alternatively be reconstituted for nebulization or injection, depending on the specific product requirements. This approach has been demonstrated with a wide variety of treatments –– including biologics –– as easy-to-administer dry powders with improved stability and retention of therapeutic integrity. The TFF technology is being applied to benefit people with a wide range of diseases and to streamline distribution of much-needed therapies around the globe.
Vaccines were once a promising approach to treating cancer, but they had mixed success in the clinic. With time and scientific progress, there has been a resurgence of interest in vaccines; however, one of the setbacks to effective cancer treatments is the hostile and suppressive tumor microenvironment (TME), which block T cell infiltration, limiting the efficacy of immunotherapies.
We believe that modulating the suppressive nature of the TME can be a transformational approach in a safe and effective way. Other approaches blocking suppressive pathways can have safety implications, as they can also target normal essential pathways. Targeting multiple immunosuppressive elements, both in tumor cells and non-cancerous cells in the TME, could open new possibilities in immunotherapy. The elimination of immunosuppressive target cells modulates the TME from an immunosuppressed environment hostile to T cells into an inflamed environment that can potentially boost T cell infiltration, enhancing the antitumor response.
TME-targeted vaccines could become the foundation for successful T cell–based therapies, since they can fine-tune the tumor to become more permissive and allow a complementary, potent immune therapy to realize its full potential. This approach could potentially work with a range of modalities or any treatment that could be aided by a microenvironment-modulating agent. At IO Biotech, our approach has the unique ability to activate naturally occurring T cells in the body to target and disrupt the immune suppressive elements of the TME to create new medicines that could bring therapeutic benefit to patients in need.
There are several exciting developments on the horizon, and one that I’m particularly interested in is the evolution of personalized medicine. The ability to identify specific populations, elucidate the mechanism of disease, and develop appropriate targeted therapies will be critical to our future approaches to drug development. For example, much of the success seen to date in oncology has come from identifying specific patients for targeted therapy, which I saw firsthand in my previous role at Daiichi Sankyo.
Another good example is osteoarthritis (OA) — the field has come to recognize that OA is not a single disease entity but represents heterogeneous populations. Evidence from epidemiologic studies shows that, within a disease like OA, some patients will have stable disease, and some will have progression, which can be seen in symptoms, structure, or both. Using several different types of data — radiographic, biomarker, gene-wide associations, and even epigenetic markers — allows identification of populations that are more likely to progress than others. The hope is that by identifying patient types, we’ll be able to better choose appropriate therapeutic modalities and include the correct patients in clinical trials.
In our clinical trials in knee OA at Xalud, we have begun to identify specific patient phenotypes that are more likely to respond to therapy. In future trials, we plan to use patient characteristics and imaging modalities in patient selection. We also would like to examine a range of serum and joint biomarkers to better understand which patients are most likely to benefit.
From a CDMO perspective, biologics growth is outpacing overall pharmaceutical industry biologics growth. Over the years, commercial manufacturing of monoclonal antibodies has become more streamlined as technological advancements and regulatory understanding evolve. First-in-class drugs are prominent for biopharma to survive in the near future. Many of the products in the first-in-class pipeline are not conventional full-length monoclonal antibodies but new molecular formats, such as bi- and multispecific antibodies, bioconjugates, enzymes, or even fusion proteins and antibody fragments. The biologics pipeline will continue to grow and include more complex molecular formats, which will bring more focus on choosing the right protein engineering technology, development of high-stability cell lines, process optimization and development, purification, analytics, and fill and finish of the final drug product.
Among the most transformative developments on the horizon, machine learning and artificial intelligence platforms offer rapidly evolving prediction tools for a broad range of bioprocessing, development, and manufacturing processes. Mechanistic and empirical modeling will improve process transfer efficiency and the development of new cell lines. Along with AI and machine learning, specialized and integrated data-management platforms will be required to support a significant increase in data sets. These new developments offer an opportunity to increase the speed to the clinic, lower the manufacturing cost of goods, and provide risk limitations. These new innovations and technology advancements will continue to transform the CDMO space and offer benefits to large pharma, mid-size biotech, and emerging biotech.
At CBM, we track innovations and build capabilities to support therapies being developed by our clients. In 2022, we are seeing more interest and investment in:
Cell Therapies
Vector & Gene Therapies
Across both modalities, we are seeing increased usage of:
The technology that will be most disruptive to life science is genetics information. The quest for sequencing the human genome was of monumental significance. It promised to change medicine –– as President Clinton said at the culmination of the human genome project: “In coming years doctors will increasingly be able to cure diseases like Alzheimer’s, Parkinson’s, diabetes and cancer by attacking their genetic roots.”
They were mostly correct. A baseline was created, but many of these diseases arise from genetic differences. These differences are now known to influence the probability of disease and the efficacy of treatments. Late in the human genome project, the connection between BRCA1 and hereditary breast cancer was being discovered.
The problem has been that there is not enough genetic information to robustly associate diseases, treatments, and outcomes with genetic profiles. Now that the cost of sequencing is falling, getting a genome sequenced is becoming more common. This information collected with health records will allow biostatistics to relate genetic profiles to the therapies that provide the best outcomes. During the peak of the COVID-19 crisis, it was clear that some patients are only mildly affected by the disease, while it was fatal to others. The increasing collection of genetic data will allow identifying genetic risk factors for COVID-19 severity and many other diseases. Additionally, genetic profiles will guide treatment by enabling selection of the most efficacious drugs for many more diseases, to replace the current trial and error process that is still the standard.
Disruptive technologies in most markets mean altering the way business is done, changing the process or outcome so significantly that the traditional systems are completely upended. In the pharmaceutical space, this can prove difficult, as regulations often define processes and deliverables, and the need to find a short path to regulatory approval outweighs innovation. Opportunities in the pharmaceutical space can lie in the ability to provide platform delivery systems that change the way that drugs are delivered.
In the topical space, many companies are looking for improved local delivery of drugs through the skin in order to bypass first-pass liver metabolism. Additionally, release over time can be required and has historically been achieved with a transdermal patch. MedPharm has created disruptive delivery platforms in the topical space through spray technology that allows occlusion and, that can exhibit the benefits of a transdermal patch without local irritation from adhesives, and that can offer true patient benefits. MedSpray™ is a transformational solution to transdermal application that offers sustained release of a drug without the compliance issues of a patch. The aerosol application on affected skin allows a patient to apply the dose without touching the affected area, and the volatiles that evaporate upon expression provide a cooling effect on the skin. The technology provides an occlusive effect on the skin, keeping out moisture while delivering the drug over time.
As we live through what I would characterize as a “golden era of biology,” there are several disruptive technologies worth highlighting, starting with mRNA (messenger RNA) medicines used as an example for COVID-19 vaccination. Soon, I would expect to see several other applications as we see the mRNA technology optimized and deployed to address other infectious diseases and the oncology space. Also, we are experiencing material advancement with targeted therapies and personalized medicine from diagnostics to cell and gene therapies, transforming the way we treat patients. Several of these advancements are at the intersection between biology and engineering, especially the digital side of it, which I consider the real disruptor of the current era, our capability through machine learning (M.L.) and artificial intelligence (A.I.) to create solutions to impact life. I think about solutions such as Alpha Fold from Deep Mind that are able to predict protein structures to the use of semiconductor technologies to sequence DNA, among many other applications of digital science to healthcare, disrupting the way we will diagnose and treat patients in the future.
The disruption taking place in the life sciences industry is multifaceted but ultimately it comes down to how organizations manage their business data and, more importantly, that data being “machine ready” –– particularly in relation to drug development.
The three technology areas influencing this are: (1) machine learning and artificial intelligence (deep learning); (2) natural language processing (NLP); and (3) blockchain.
The growing maturity and fundamental improvement in artificial intelligence will see this technology have a material positive impact in the life sciences industry. Combined, machine learning, artificial intelligence, and natural language processing will enhance the positive impact through algorithms that can self-learn.
The outcome of these advancements in technology will enable life sciences companies to develop targeted therapeutic treatments faster, more efficiently, with lower costs, that are more accessible to patients.
One additional technology area that I believe will cause disruption is blockchain. Blockchain provides a decentralized distributed public ledger to record transactions. It guarantees the fidelity and security of a record of data and generates trust without the need for a trusted third party. Three potential use cases in life sciences are drug development, clinical trials management, and supply chain optimization.
The VB Secretion technology that is being developed by Vectron will become the new standard of microbial protein production. Getting proteins out of the E. coli cell and thus reducing downstream costs and avoiding issues with inclusion body formation and protein degradation, has been the holy grail of E. coli protein production for decades. We are proud and privileged to lead this revolution by developing the first true secretory E. coli strains to be marketed in 2023. Preliminary results of selected high-value proteins indicate secretion at > 1 g/L levels with a purity of >95%.
We believe that an ongoing blurring of lines between document and data, with the technologies which support this, is going to best support the existing reliance on documents and the growing desire for agencies to receive data. Documents are of course a convenient way to consume information: but they should no longer be “black boxes.” Technology to extract data from documents and to build other documents from data (using different combinations automated based on rules and context) is what we think will bring the greatest benefit. At least until AI goes out of control and takes over everything!
Many biopharmaceutical functions have spent the last decade modernizing their base technologies, most often in a cloud/software as a service environment platform that brings foundational benefit to individual functions. We are in the early stages of the “data connectivity” era, where critical data from various functions (regulatory, quality, manufacturing, safety, clinical, etc.) is slowly being connected, driven by internal productivity and external regulatory requirement factors. This requires a clear focus and excellence on cross-functional data governance, master data management, and ensuring that all data from these various authoritative systems is at the same high level; this provides “data quality confidence”, which directly impacts productivity and effective decision-making.
The impact will be substantial to improving time from clinical development to patient product access (especially in smaller markets) and reducing life cycle management complexity, which in turns reduces cost and compliance risk. Better-connected data also enhances partnering and better health authority information exchange; it is truly the beginning of the data science era!
Digital regulatory transformation is to a large extent related to the move from document centricity to becoming data driven. Structured content authoring is also making strides. But what is still somewhat missing today, is intelligent automation (such as AI and robotic process automation) to drive efficiency. Even though the technologies exist, they are still heavily underutilized. Only by eliminating tedious manual tasks by intelligent tools and bots will industry be able to truly achieve efficiencies. As an example, in “end-to-end labelling,” there is an opportunity to fully digitize the information supply chain from core labeling toward local label production, including intermediate processes like conformance tracking, proofreading, language translation, artwork production, printed packaging materials, and last but not least the medicinal product data submissions. More generally, in the space of regulatory data submissions, the linear paradigm of dossier-based submissions is at risk of being disrupted by initiatives like Accumulus Synergy, a not-for-profit organization that is implementing a global cloud-based information exchange platform to allow fast, incremental, and collaborative data exchanges between industry and regulators.
As innovative drugs continue to emerge, so does the need for advancements in technology and industrialization. Strategic partnering and contract services are becoming the new norm to tackle new challenges, and the contract development and manufacturing organization (CDMO) industry is now seeing a new round of booming growth. CDMOs, which develop and manufacture GMP active ingredients, need to adopt digital tools that offer flexibility and agility, both in R&D and production plants.
The key to digital transformation is a strategic organizational decision that involves the entire manufacturing process to offer end-to-end solutions and services. For this reason, we at Porton Advanced implemented a digital transformation process that enables us to respond to the full needs of each individual customer across multiple therapeutic platforms.
Theranostics refers to the pairing of diagnostic biomarkers with therapeutic agents that share specific targets in diseased cells or tissues. Nuclear medicine, particularly regarding applications in oncology, is currently one of the greatest components of the theranostics concept in clinical and research scenarios. Theranostics in nuclear medicine pertains to the use of radionuclides to image biologic phenomena by means of expression of specific disease targets, such as cell surface receptors or membrane transporters, and then to use specifically designed agents to deliver ionizing radiation to the tissues that express these targets. The nuclear theranostics approach has sparked increasing interest and gained importance in parallel to the growth in molecular imaging and personalized medicine, thereby facilitating the customized management for various diseases. This have resulted in improved patient selection, prediction of response and toxicity, and determination of prognosis, avoiding futile and costly diagnostic examinations and treatment of many diseases.
For many applications areas, such as protein therapeutics and viral vector production, great strides have been made in scalability by developing suspension-adapted cell lines. Alternatively, scalability is still a major challenge for regenerative medicine applications and stem cell–derived therapeutics. Adherent platforms are still the clearly preferred method for expanding these cell types because they maintain the biology, “stemness,” and critical-to-quality attributes. Multi-layer 2D planar technologies are a great choice for early-phase production but suffer scalability challenges at commercial scale. Microcarriers paired with suspension bioreactors attempt to bridge the gap, but they can be difficult to optimize and may introduce shear stress, leading to an impact on the biology. Fixed-bed reactor technology can provide a low-shear, scalable, cell expansion solution, but first- generation technology offerings have not enabled high-efficiency cell harvest. The next-generation Corning® Ascent® Fixed Bed reactor utilizes an innovative substrate design paired with automation capabilities to enable the large-scale expansion of anchorage-dependent cell types and the ability to automate high-efficiency cell harvest. This technology offers the potential for transformational scalability of difficult-to-scale cellular therapeutics.
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