Innovations in Clinical Pharmacology: Shaping the Future of Evidence Generation in Research, Development, and Utilization of Medicines

The field of Clinical Pharmacology has undergone remarkable transformations over the past decade, driven by advancements in technology, science, and our understanding of human biology that have redefined drug development and patient care. Innovativeness, strategic context, and clinical relevance, with a heavy emphasis on computational sciences and precision medicine, have been foundational pillars supporting the content published in Clinical Pharmacology and Therapeutics (CPT) over the last 5 years.1 The steadfast commitment to developing and empowering the next generation of scientists and leaders has characterized the Journal.2 In this issue, we showcase contributions through a lens of innovations enabling evidence generation in the development and practice of medicine. Framed by a State of the Art review on the history and maturation of impactful innovations in clinical pharmacology,3 this issue highlights opportunities for our discipline, exploiting multimodal data, next-generation translational technologies, and advanced analytics in enabling sustainable journeys from molecules to medicines. An important purpose of clinical pharmacology is to maximize the benefit vs. risk of medicines and efficiently enable access at the right dosage for all patients, as explored in depth in the March 2023 issue of CPT.4 This has been enabled by innovations that continue to advance our understanding of the complex interplay of intrinsic and extrinsic factors that govern population variability in drug exposure and response. The integration of omics sciences—spanning pharmacogenomics, proteomics, and the gut microbiome—has enabled unprecedented insights into the genetic, molecular, and environmental determinants of therapeutic outcomes. As discussed by Caudle et al.,5 the Clinical Pharmacogenetics Implementation Consortium (CPIC) has set the global standard in clinical pharmacogenomics by providing free, evidence-based guidelines that translate genetic test results into actionable prescribing decisions for 34 genes and 164 drugs, with a focus on global reach and education to remove remaining barriers for broader adoption in clinical practice. CPT has been a home for numerous impactful CPIC guidelines, including one in the current issue detailing evidence relevant to the drug-metabolizing enzyme N-acetyl transferase 2 (NAT2) and recommendations for hydralazine prescribing based on NAT2 genotype-predicted acetylator phenotype.6 Viewed from a broader perspective beyond hydralazine, this CPIC guideline provides a seminal reference to clinical pharmacologists who may be engaged in the discovery and development of investigational agents with NAT2-mediated clearance, where pharmacogenetic considerations will be vital to defining appropriate dosing across populations. The gut microbiome, now recognized as a key modulator of drug metabolism and response, adds another layer of complexity and opportunity for individualized therapy, ushering in the emerging era of pharmacomicrobiomics. An intriguing example is offered by Saqr et al., in patients with hematologic malignancies undergoing hematopoietic cell transplantation and post-transplant immunosuppression with mycophenolate, where the authors demonstrate a link between the abundance of β-glucuronidase producing stool microbial communities and achievement of target exposures of mycophenolic acid. Of note, these findings were quantitatively described in mechanism-based population pharmacokinetic models, contextualized by a key role of glucuronidation, gut microbial deconjugation, and enterohepatic recirculation in mycophenolate disposition.7 In their compelling perspective on the topic, Haiser and de Gruijl8 offer a call to action for microbiome-aware drug development, discussing examples of the role of gut microbes as determinants of benefit–risk of therapeutics and translational technologies for early drug development. As illustrated by many research contributions in this issue, advances in translational tools for in vitro–in vivo extrapolation,9 technologies for pharmacogenomic profiling,10 emerging biomarkers,11 and experimental medicine platforms integrated into model-based quantitative frameworks12, 13 are enabling the timely generation and clinical testing of hypotheses regarding the determinants of inter-patient variability in drug exposure and response. These examples not only highlight scientific progress but also point to the future opportunities that remain for our discipline in translating these insights into scalable drug development paradigms and clinical care, such as the need for robust bioinformatics infrastructure, evidentiary frameworks, and clinician education. The translation of preclinical data of new molecular entities to predict the anticipated therapeutic window and inform safe and efficient early clinical investigation at appropriate doses is a key focus of clinical pharmacologists engaged in pharmaceutical research and development. With biotherapeutics such as certain monoclonal antibodies and immune agonist mechanisms, the limited fidelity of preclinical models for human translation remains a significant challenge. Aligned with goals for sustainable preclinical research and considering the limitations of traditional animal testing, the development of new approach methodologies (NAMs) that aim to be more informative and efficient alternatives is an important area of innovation, as reinforced recently by the US FDA14 with opportunities noted across multiple contexts of use.15 In their perspective on this topic, Cao and Polacheck highlight opportunities for clinical pharmacologists to engage in interdisciplinary collaborations with NAM engineers to define the right contexts of use of NAMs, and leverage advances in data sciences, physiologically based pharmacokinetic (PBPK) modeling and quantitative systems pharmacology (QSP) modeling, to enhance fidelity in clinical translation.16 In his perspective on NAM characterization and qualification, Prasad posits that quantitative proteomics is pivotal to the advancement of NAMs, especially microphysiological systems and modeling platforms such as PBPK and QSP.17 While these calls to action for NAMs in lieu of animal testing highlight new opportunities, we must approach them with optimism reflecting on the rich history of clinical pharmacology innovations that have yielded sustainable evidentiary frameworks, such as predictive models of drug–drug interactions and virtual bioequivalence studies that subscribe to Totality of Evidence principles.3 Novel therapeutic modalities, including mRNA-based therapies, siRNA, gene therapies, advanced drug delivery systems, cell therapies, and next-generation vaccines, are rapidly changing the landscape of drug development and the practice of medicine. CPT’s September 2023 issue18 was dedicated to the topic of novel modalities, highlighting the critical importance of our discipline in accelerating their path from bench to bedside and enabling benefit–risk assessment. In the present issue, Van et al.,19 in their State of the Art review, provide a comprehensive overview of the role of clinical, translational, and quantitative pharmacology in the development of mRNA-based therapeutics and vaccines, including opportunities for NAMs for advancing clinical translation. A deep dive into model-informed drug development frameworks for this emerging modality is offered in the review article by Zhou et al.20 These comprehensive reviews highlight the breadth of knowledge and expertise across molecular biology, immunology, bioanalysis, drug delivery technologies, PBPK, QSP, pharmacometrics, and data sciences that clinical pharmacologists working with such complex modalities need to acquire. Vital to sustainable innovation is a commitment to continuous learning with a growth mindset and collaborative spirit. With many emerging therapeutic modalities, clinical pharmacologists will need to rethink deeply even the most basic aspects, such as interpretation of PK measurements from first principles, in the context of events at the site of action. This often requires innovative mechanistic paradigms for population PK modeling leveraging the totality of preclinical and clinical data, as illustrated by Ogawa et al.21 for a combination product consisting of two N-acetylgalactosamine-conjugated short-interfering RNA molecules in development as a potential treatment for chronic hepatitis B virus infection. As a data-driven discipline, clinical pharmacology has consistently leveraged quantitative analytical methods to integrate the totality of data collected across diverse settings ranging from the experimental laboratory to the bedside in clinical care settings, enabling data-driven hypotheses and evidence-based decisions in the research, development, and use of established and emerging therapies. The rapid growth of computational and data sciences, in terms of both the expanding diversity of data modalities and the emergence of artificial intelligence and machine learning methods, has steadily propelled the discipline. This has been notable over the last 5 years, as showcased comprehensively in CPT’s seminal themed issues published in April 202022 and April 2024.23 Advances in large language models are creating unprecedented opportunities, with steadily expanding contexts of use.24 A White Paper by Krishna et al.25 in the present issue of CPT highlights how data sciences and model-informed drug development are transforming therapeutic development for pediatric rare diseases by enabling efficient, data-driven decision making and reducing reliance on large clinical trials. The authors identify opportunities for the integration of digital biomarkers, patient-reported outcomes, and modeling techniques, such as extrapolation from adult data and digital twins, to personalize treatments and accelerate drug approvals for small, heterogeneous pediatric populations. Furthermore, they posit that data sharing is critical for progress in pediatric rare disease development, as it allows all stakeholders to pool knowledge, expertise, and datasets, with the ability to consolidate large, diverse datasets enabling enhanced robustness and predictive power of novel biomarkers of disease. The power of data sharing is also illustrated by Tompkins et al.,26 who introduce the HIV Pharmacology Data Repository, a standardized, Web-based platform for sharing PK data from HIV research. By implementing a minimum information standard across three categories—Intervention, System, and Concentration—the repository enables real-time data sharing, supports model-informed drug development, and facilitates advanced pharmacometric analyses to accelerate therapeutic innovation. A notable example that illustrates innovation without boundaries in the use of multimodal data is the work of Liu et al.27 in drug repurposing. In this study, multi-omics data including genomics, methylomics, metabolomics, and transcriptomics were integrated to describe an osteoporosis disease network. These networks were then combined with drug functional networks built from drug similarity data (chemical structure, gene expression, text mining, and in vitro tests) to screen over 10,000 compounds for potential therapeutic effects in osteoporosis. The translational informatics analyses identified the antihypertensive beta-adrenergic antagonist acebutolol as a potential lead, with the therapeutic hypothesis further evaluated in a zebrafish experimental model and reinforced by analyses of real-world bone mineral density data in patients taking beta-adrenergic antagonists for cardiovascular indications. While the research is expansive and impressive from a technical perspective, it is important to note that this is hypothesis generating, with clinical translation remaining to be established. Many questions remain for quantitative clinical pharmacologists, including the precise definition of the therapeutic window and associated dosage for the desired effects of acebutolol or another beta-adrenergic antagonist in osteoporosis, necessitating prospective evaluation of dose/exposure–response relationships to optimize dosage for hypothesis testing.28 As the clinical evaluation of treatments for osteoporosis will require large trials of long duration, it is important that model-informed drug development principles29, 30 and quantitative methods to forecast probability of success31 are rigorously applied to guide next steps in clinical translation. Integrated evidence generation for the assessment of benefit–risk and the value of medicines is being rapidly reshaped by real-world data (RWD), with notable advances highlighted in CPT issues published in May 2021,32 January 2022,33 November 2024,34 and April 2025.35 There is a growing recognition of the value of incorporating “pragmatic elements” in clinical trials, characterized by broad eligibility criteria, design elements more akin to clinical practice settings, patient-centered outcome measures, and decentralized elements, and RWD-informed design and/or conduct. Su et al.36 present a targeted review of clinical trials published during 2016–2024 and identify 22 trials with pragmatic elements and five trials that incorporated RWD in clinical trial extension settings. The findings point to the value of such hybrid trial designs for enhancing the relevance, efficiency, and generalizability of clinical research, while pointing to remaining challenges and areas for future research to increase broader adoption and value. Hybrid designs that leverage external control arms based on RWD are an important avenue for evidence generation from single-arm trials or from randomized controlled trials (RCT) with reduced sample size or trial duration when traditionally designed randomized controlled trials may not be feasible. In a review of the growing use of RWD in regulatory drug and regenerative medicine approvals in Japan by Okami et al.,37 a major context of use was in support of comparator arms in clinical trials where RCTs were ethically or practically infeasible, especially in pediatric and rare disease contexts. Two research articles in this issue present findings that highlight opportunities while pointing to the challenges that remain to fully realize the value of such designs. Zhao et al.38 illustrate the application of a hybrid design of a Phase III RCT in the ophthalmology therapeutic area in China, with Bayesian borrowing of prior data from three sources—a global RCT, a regional Phase III RCT in China, and RWD in China. Russek et al.39 retrospectively surveyed single-arm clinical trials registered in the European Union’s clinical trial registers between 2004 and 2023 in two very different disease contexts of breast cancer and amyotrophic lateral sclerosis, with the objective of assessing the feasibility of RWD-derived external control arm supplementation based on five German RWD sources. Their research indicated that very few single-arm trials could feasibly be supplemented with external control arms if all important eligibility criteria and a primary endpoint had to be identifiable in the external RWD sources. These observations underscore the importance of designing such hybrid trials with careful consideration of the information content in the external RWD source(s), making thorough scholarship and timely engagement of real-world evidence experts important in integrated evidence planning in drug development. Innovations across the breadth of clinical pharmacology and adjacent disciplines are rapidly reshaping the future of end-to-end evidence generation in the discovery, development, regulation, and utilization of medicines. As evident from the breadth of contributions in this issue from across the Americas, Africa, Europe, and Asia, including research and regulatory science efforts that are enabling increased benefit vs. risk and decreased access lag for current and future medicines, the impact of clinical pharmacology resonates at a global scale.3, 40-42 To keep up with the pace of innovation that is necessary to address current challenges in health care with urgency, there is a growing need for training programs in biomedical research that develop scientists with the diverse skill sets needed to navigate complex biological systems, integrate emerging technologies, and collaborate across disciplines to drive innovation and translational impact. This will additionally require bringing together scientists across borders in industry and academia through mechanisms such as internships, postdoctoral fellowships, and sabbaticals aimed at reinforcing the resilience of the broader ecosystem for sustainable innovation with a growth mindset. We trust that the present issue of CPT offers a valuable sampling of many important themes of emerging innovations that will inspire our readers across sectors of practice to continue their journeys toward transformative innovation for sustainable and evidence-based research, development, and utilization of the next generation of medicines for all patients. No funding was received for this work. The authors declared no competing interests for this work.