Precision medicine leverages increased genetic understanding through the application of advanced molecular and systems biology techniques to develop medicines that are more highly targeted to specific disease subtypes. Physicians select the treatment most suitable for a given patient’s genetic makeup, as well as their environment and lifestyle. Because the term personalized medicine evokes the notion that treatments are being developed for individuals, the National Research Council in 2011 recommended the use of the term precision medicine.1
Technologies used in precision medicine include bioinformatics; big data analytics, including artificial intelligence (AI) and machine learning (ML); drug discovery; gene sequencing; many different omics fields; and companion diagnostics. Most precision medicines treat various cancers, but there are therapies that target respiratory diseases, central nervous disorders, immunology, genetic diseases, and others.
In four of the last five years, precision medicines have accounted for approximately 35% of all therapeutic molecular entities approved by the U.S. Food and Drug Administration (FDA).2 For each of the last seven years, they have accounted for more than 25% of novel approved drugs. In addition, many significant new personalized medicine indications for existing drugs and combinations of drugs have been approved in recent years. Simultaneously, the FDA’s Center for Devices and Radiological Health (CDRH) has approved or cleared several significant new or expanded indications within in vitro diagnostic testing applications that underpin personalized medicine strategies. It is also worth noting that, in 2021, the FDA granted approval to a diagnostic panel test that identifies advanced cancer patients with solid tumors of all types that have deficient mismatch repair (dMMR) and granted recognition to a partial listing of the Memorial Sloan Kettering Cancer Center’s Oncology Knowledge Base (OncoKB) as the first tumor mutation database to be included in the FDA’s database recognition program.
With all of the FDA approvals, it should not be surprising that the market for precision medicines is rapidly expanding. Different market research firms get the compound annual growth rate for the global market at 11.5– 13%.3-5 The estimated value varies widely, however, from approximately $33 to $73 billion in 2021 and rising to $134.82 billion in 2026,3 $64.66 billion by 2027,4 and $175.64 billion by 2030.5
Although companion diagnostics are essential to most precision therapies, only a few of the top pharmaceutical companies, such as Roche, Abbott, and Novartis, have established core competencies in biomarker and diagnostics development. Most precision medicine developers rely on smaller biotech and diagnostic firms to launch companion diagnostics, leading to extensive deal activity in this space, with more than 60 deals finalized in the last five years.5 In the coming five years, investment in precision medicine from leading pharmaceutical companies is expected to grow by a third.6
There is a reason that so many candidates in the pharmaceutical pipeline are precision medicines: they bring tremendous benefits to both drug developers and patients.
For pharmaceutical companies, greater knowledge and understanding of disease mechanisms and the genetics of disease combined with access to vast quantities of genomic and traditional data and the software tools (AI and ML) to evaluate them are reducing the time and cost to develop successful targeted therapies.6 In addition to accelerating drug discovery and design, precision medicine that involves multi-level patient stratification holds the promise of optimizing the cost, time, and success rate of clinical trials via the co-development of drugs (Rx) and diagnostics (Dx) for future targeted therapies.7 Medicines designed to treat a condition with specific genetic characteristics have greater efficacy for the targeted patient population, leading to greater market success and improved brand recognition.
Patients receiving precision medicines have a much greater than 50% chance of benefiting from that treatment, which is unfortunately the typical success rate for traditional medicines developed using traditional approaches that target the “average” patient.6 Doctors can more accurately diagnose patient diseases with access to genomic data. Increasing numbers of pharmacogenomic trials are also building databases with information on how quickly the body breaks down drugs and how likely certain side effects are to occur, ultimately leading to the selection of effective treatments with the fewest possible side effects. The latter is important, as reactions to undesired side effects account for approximately 30% of acute hospital admissions annually. Ideally, precision medicines will not only provide better response rates with fewer side effects but also reduce treatment times.
Given the gap that currently exists between how many patients could and how many actually received precision therapies, one of the biggest hurdles to expanding precision medicine is increasing access for patients to the latest diagnostic tools, including next-generation sequencing (NGS) and liquid biopsies.8 Payers (insurance companies) need to be educated on the value provided by genomic testing and precision therapies, while doctors need to be educated on the use of genomic testing and interpretation of the results. Patient communication must be improved as well.9 There is also a need to improve NGS/genomic sequencing panel (GSP) biomarker testing with respect to the required sample quantities, turnaround times, and costs.
There are also ethical, legal, and social implications of precision medicine that must be addressed, including complexities around informed consent; health disparities due to cost, access issues, and under-representation of minority groups in research; and individual and group privacy and confidentiality issues with respect to the sharing of genetic information.10 Sharing data in different formats and systems is a further challenge.11 Standardization of data collection and use has yet to occur.12 These concerns can only be overcome with appropriate regulations and oversight, which is currently lacking.10,11
Other issues relate to the need for massive data storage and the existence of complex supply chains, such as for autologous cell therapies. Combined with inefficient regulatory processes, these problems are creating barriers and bottlenecks that only further enhance the inequitable delivery of precision medicines.11 Traditional large-scale, placebo-based clinical trials are also inappropriate for targeted medicines intended to treat small patient populations, requiring new approaches to establishing the long-term safety, efficacy, and durability of precision medicine treatments.
What is needed, according to some observers, is an agile governance approach that “improves government coordination, leverage public–private partnerships, and actualize the potential of personalized healthcare without further exacerbating health inequity.”11 The key is for policy makers and governments to work alongside technology researchers and developers in order to capitalize on the expertise of the private sector and academia and ultimately enhance how regulators respond to the emergence of new technologies.
Advances in big data analytics technologies will play a huge role in making precision medicine a standard of care for all patients in the future. In addition to enabling the analysis of large quantities of disparate data (e.g., genetic, clinical, and socioeconomic data), AI and ML will help with drug design and the identification of patients who can benefit most from a given precision medicine.9 On a more general note, precision medicine serves as a promising means for preventing and treating — rather than simply managing — chronic diseases that leave people living with poor health conditions with the ongoing need for high-cost medical care.7
To improve patient diagnoses, the design of therapeutic interventions and the determination of prognoses requires analysis of large and complex data sets comprising genetic, functional, and environmental information from sources ranging from longitudinal multi-omic data sets to electronic medical records.13 AI / ML / deep learning (DL) algorithms can learn from heterogeneous data sets and discover new drug targets, repurpose the current existing ones, or eventually guide the decision-making protocol. Indeed, AI and ML are improving the design of both preclinical experiments and clinical trials and helping to match patients to the right precision therapies.
For instance, companies such as IBM Watson, N-of-One (a molecular decision-support system provider), and 2bPrecise LLC (an Allscripts company) have developed advanced clinical decision-support solutions that can analyze genomic, clinical, and lifestyle data.7 Such learning health systems integrate genomics data into the clinical workflow and are expected to experience growing use over the next few years.
One important issue, however, is that not only the sources and diversity but the quality and reliability of the data used by AI and ML algorithms will determine the value of the results obtained.13,14 Accurate data training and the collection of data from diverse populations are therefore essential.13 So is testing algorithms to ensure that they replicate real-world data.14 Crowd-sourcing, as well as collaborations between drug developers and various institutes, patient advocacy groups, and other research entities, will help address these issues.13 Access to sufficient computer power is another challenge, which some companies are addressing through the use of cloud computing and parallel processing systems.14 Simultaneously, companies must ensure the security of patient data used by AI/ML systems and that fairness, accountability, transparency, and trust are upheld.14
Researchers at Rutgers Health have developed a mobile app that attempts to address at least some of these issues, as well as the need for more education and communication. Known as PAS, the app allows scientists and healthcare providers to quickly and easily search a comprehensive worldwide database of genes, variants, and related diseases and drugs with the goal of helping researchers, medical practitioners, pharmacists, life-science students, and even patients better understand the genetic basis of common diseases and map health conditions to their corresponding diseases.15
Software and hardware advances are not the only technology developments that will facilitate progress in precision medicine. Advances in next-generation analytics technologies will be equally important. Fortunately, many companies are making significant investments to improve traditional molecular and genomic tests and develop new proteomic-, metabolomic-, and microbiome-based assays.9
The greatest advances in precision medicine to date have occurred in the field of oncology. As genetic understanding has increased with respect to the existence of distinct subtypes that progress and respond to treatment in different ways, precision medicine approaches have shown tremendous promise in colorectal, breast, lung, esophageal, stomach, ovarian, and thyroid cancers, certain leukemia and lymphoma subtypes, and melanoma.8 Many candidates in clinical trials also target genetic mutations within specific cancers in combination with the development of diagnostic tools to detect those mutations and the identification of biomarkers to help predict which patients will respond to specific treatments. This focus on cancer is not surprising, given that traditional chemotherapy agents often have only a 20% success rate.6
An exciting development in the field of precision medicine is the growing interest in developing targeted therapies for diseases other than cancer. It has been reported that two-thirds of phase III precision medicine pipelines are focused on non-oncology areas and more than 90% are diagnostic-dependent.6 Areas of focus include infectious, central nervous system (CNS), and cardiovascular diseases, as well as diabetes. Two CNS disorders receiving particular attention include Parkinson’s and Alzheimer’s diseases.
Real-word data (RWD) is being used to determine how different phenotypes respond to different cancer drugs, while real-word evidence (RWE) is being applied to better match patients to specific cancer treatments. This approach should also be applied in other disease areas to increase the applicability of precision medicine and to boost the rate of positive treatment outcomes.16 The challenge, in many cases, is that there are many medications available with no clear means of selecting the best drug for a given patient. Because many of these drugs are off-patent, there is no obvious and immediate incentive for manufacturers to invest in the generation of RWD and RWE.
IQVIA is one company looking to tackle this issue. Its Subpopulation Optimization & Modeling Solutions (SOMS) offering is an AI/ML-based technology that can analyze a combination of clinical trial data and RWD to segment populations based on a variety of attributes, including disease state, genomic, proteomic, transcriptomic, tumor size, demographics, and socio-economics, to provide unique patient segmentation insights that can be leveraged to enable precision medicine.17
Companies should recognize, however, that using RWD and RWE to “personalize” medical treatments for indications beyond cancer will reduce health disparity while increasing positive outcomes and lower overall insurance and healthcare costs. It will also simultaneously expand the understanding of diseases and disease mechanisms, knowledge that can facilitate future drug discovery and development efforts. In addition, small-cohort RWE can help overcome the limitations of traditional clinical trials for rare disease therapies.17,18
While precision medicine has the potential to change the way that diseases are treated, questions about accessibility remain. The cost of genomic sequencing is an important limiting factor; a typical test can run more than $5,000, and coverage by insurance companies varies greatly. Gene therapies and gene-modified cell therapies, two leading categories of precision medicines, can cost hundreds of thousands to millions of dollars, creating the need for new approaches to payer coverage.
Given that precision medicine is largely driven by data, a number of groups have emerged with the intent of encouraging precision medicine as well as gathering and curating data specifically for use in precision medicine applications. The Personalized Medicine Coalition brings together drug companies, physicians, payers, and patients to promote the understanding and adoption of personalized medicine concepts, services, and products to benefit patients and health systems.19
The Precision Medicine Initiative (PMI) in the United States aims to initially expand precision medicine in cancer research but has long-term hopes to bring prevision medicine to all areas of health and healthcare. As part of this effort, the National Institutes of Health launched the All of Us Research Program, to which at least 1 million volunteers are contributing genetic data, biological samples, and other information about their health.20
Many other countries have also independently launched national genomic-medicine initiatives.11 Meanwhile, the Global Alliance for Genomics and Health, the International Rare Diseases Research Consortium (IRDiRC), and the Privacy-Preserving Record Linkage (PPRL) Task Force are focused on developing solutions to enable responsible sharing of genomic and clinical data internationally.
The International Consortium for Personalized Medicine (ICPerMed) focuses on five perspectives of precision medicine: individual and public engagement, involvement of health professionals, implementation within healthcare systems, health-related data, and the development of sustainable economic models that allow improved therapy, diagnostic, and preventive approaches.21 ICPerMed “believes that advancement of the biomedical, social, and economic sciences, together with technological development, is the driving force for precision medicine,” and by focusing on these five main perspectives can ultimately achieve precision medicine as a “medical practice centered on the individual’s characteristics, leading to improved effectiveness of diagnostics, treatment, and prevention, added economic value, and equitable access for all citizens.”
Precision medicine, made possible by novel digital and analytical technologies, is impacting all aspects of drug discovery and development and is beginning to change traditional concepts regarding patient treatment. As a result, medicine is transitioning from being serendipity-driven to being data-driven.22 “Tailored digital technologies and patient-centric drug development, linked to a broader paradigm shift from one-size-fits-all medicine towards precision medicine (the right medicine, for the right patient, at the right dose, at the right time)” requires both “translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development.”
The evolution to translational precision medicine will not be easy or smooth. There are technological, ethical, legal, social, and cultural hurdles that must be overcome. Such a paradigm shift will require “engineering advances in sensing, computing, communication, and low energy cloud/fog technologies, along with new modeling and computational approaches to leverage big data, such as artificial intelligence and neuromorphic systems, and such as the design and development of components of a specific data infrastructure and subclass of the Internet of Things called the Internet of Healthcare (IoH).”23 It will also require “the systematic access and integration of research and health care at a large scale and possibly across institutions and countries,” and, as a result, “identifying reliable tools to integrate datasets remains one of the most daunting challenges faced by the field.”
The COVID-19 pandemic has shown how emerging concepts can be rapidly and successfully implemented to afford safe and effective treatments when the entire industry works collaboratively.22 Exciting new modalities — gene and gene-modified cell therapies, RNA-based treatments, patient-derived organoids, multispecific and other targeted antibody-based drugs — hold great promise for the future if new molecular and digital technologies can be appropriately leveraged. Hopefully the research community, companies, regulators, payers, and patients are ready to develop and accept solutions for data collection, sharing, and analysis that will enable true translational precision medicine.
David is Scientific Editor in Chief of the Pharma’s Almanac content enterprise, responsible for directing and generating industry, scientific and research-based content, including client-owned strategic content, in addition to serving as Scientific Research Director for That's Nice. Before joining That’s Nice, David served as a scientific editor for the multidisciplinary scientific journal Annals of the New York Academy of Sciences. He received a B.A. in Biology from New York University in 1999 and a Ph.D. in Genetics and Development from Columbia University in 2008.