Asia-inclusive drug development leveraging principles of ICH E5 and E17 guidelines: Case studies illustrating quantitative clinical pharmacology as a foundational enabler.

With the International Conference on Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) E17 guidelines in effect from 2018, the design of Asia-inclusive multiregional clinical trials (MRCTs) has been streamlined, thereby enabling efficient simultaneous global development. Furthermore, with the recent regulatory reforms in China and its drug administration joining the ICH as a full regulatory member, early participation of China in the global clinical development of novel investigational drugs is now feasible. This would also allow for inclusion of the region in the geographic footprint of pivotal MRCTs leveraging principles of the ICH E5 and E17. Herein, we describe recent case examples of model-informed Asia-inclusive global clinical development in the EMD Serono portfolio, as applied to the ataxia telangiectasia and Rad3-related inhibitors, tuvusertib and berzosertib (oncology), the toll-like receptor 7/8 antagonist, enpatoran (autoimmune diseases), the mesenchymal-epithelial transition factor inhibitor tepotinib (oncology), and the antimetabolite cladribine (neuroimmunological disease). Through these case studies, we illustrate pragmatic approaches to ethnic sensitivity assessments and the application of a model-informed drug development toolkit including population pharmacokinetic/pharmacodynamic modeling and pharmacometric disease progression modeling and simulation to enable early conduct of Asia-inclusive MRCTs. These examples demonstrate the value of a Totality of Evidence approach where every patient's data matter for de-risking ethnic sensitivity to inter-population variations in drug- and disease-related intrinsic and extrinsic factors, enabling inclusive global development strategies and timely evidence generation for characterizing benefit/risk of the proposed dosage in Asian populations.

On placental and lactational transfer of IgG-based therapeutic proteins - Current understanding and knowledge gaps from a clinical pharmacology perspective.

Maternal medication use may expose the developing fetus through placental transfer or the infant through lactational transfer. Because pregnant and lactating individuals have been historically excluded from early drug development trials, there is often limited to no human data available to inform pharmacokinetics (PK) and safety in these populations at the time of drug approval. We describe the known mechanisms of placental or lactational transfer of IgG-based therapeutic proteins and use clinical examples to highlight the potential for fetal or infant exposure during pregnancy and lactation. Placental transfer of IgG-based therapeutic proteins may result in systemic exposure to the developing fetus. A lactational transfer may be associated with local gastrointestinal (GI) exposure in the infant and may also result in systemic exposure, although data are very limited as proteins have shown instability in the GI tract. Understanding of PK and pharmacodynamic (PD) effects of IgG-based therapeutic proteins in infants exposed in utero as well as the potential exposure through human milk and its clinical implications is critical for developing treatment strategies for pregnant or lactating individuals. We share the current knowledge gaps and considerations for future evaluations to inform PK, PD, and the safety of IgG-based therapeutic proteins for safe use during pregnancy and lactation. With the increasing use of IgG-based therapeutic proteins in treating chronic diseases during pregnancy and lactation, there is a need to improve the quantity and quality of data to inform the safe use in pregnant and lactating individuals.

Clinical pharmacology in adolescent transgender medicine.

Adolescent transgender medicine is a growing clinical field. Gender-affirming medications for transgender youth may include gonadotropin-releasing hormone (GnRH) agonists, gender-affirming hormones or both. To evaluate the potential effects of GnRH agonists (puberty suppression) on pharmacokinetic processes for transgender youth, we searched PubMed from inception to May 2024 for publications on the effects of GnRH agonists on drug absorption, distribution, metabolism or excretion for transgender adolescents or effects on hormones (including gonadotropins, adrenal androgens, sex steroids) that are associated with changes in drug metabolism during puberty in the general adolescent population. No publications discussed the effects of GnRH agonist treatment on pharmacokinetic processes for adolescent transgender people. Sixteen publications observed marked decreases in gonadotropins and sex steroids for both adolescent transgender men and adolescent transgender women and slight effects on adrenal androgens. During GnRH agonist treatment, changes in body composition and body shape were greater for adolescent transgender people than for cisgender adolescent people. Further research is needed to better understand the effects of GnRH agonists on drug metabolism and other pharmacokinetic processes for transgender adolescents receiving GnRH agonists and other gender-affirming medications.

Bridging pharmacology and neural networks: A deep dive into neural ordinary differential equations.

The advent of machine learning has led to innovative approaches in dealing with clinical data. Among these, Neural Ordinary Differential Equations (Neural ODEs), hybrid models merging mechanistic with deep learning models have shown promise in accurately modeling continuous dynamical systems. Although initial applications of Neural ODEs in the field of model-informed drug development and clinical pharmacology are becoming evident, applying these models to actual clinical trial datasets-characterized by sparse and irregularly timed measurements-poses several challenges. Traditional models often have limitations with sparse data, highlighting the urgent need to address this issue, potentially through the use of assumptions. This review examines the fundamentals of Neural ODEs, their ability to handle sparse and irregular data, and their applications in model-informed drug development.

Dosing Strategies and Quantitative Clinical Pharmacology for Bispecific T-Cell Engagers Development in Oncology.

Bispecific T-cell Engagers (TCEs) are promising anti-cancer treatments that bind to both the CD3 receptors on T cells and an antigen on the surface of tumor cells, creating an immune synapse, leading to killing of malignant tumor cells. These novel therapies have unique development challenges, with specific safety risks of cytokine release syndrome. These on-target adverse events fortunately can be mitigated and deconvoluted from efficacy via innovative dosing strategies, making clinical pharmacology key in the development of these therapies. This review assesses dose selection and the role of quantitative clinical pharmacology in the development of the first eight approved TCEs. Model informed drug development (MIDD) strategies can be used at every stage to guide TCE development. Mechanistic modeling approaches allow for (1) efficacious yet safe first-in-human dose selection as compared with in vitro minimum anticipated biological effect level (MABEL) approach; (2) rapid escalation and reducing number of patients with subtherapeutic doses through model-based adaptive design; (3) virtual testing of different step-up dosing regimens that may not be feasible to be evaluated in the clinic; and (4) selection and justification of the optimal clinical step-up and full treatment doses. As the knowledge base around TCEs continues to grow, the relevance and utilization of MIDD strategies for supporting the development and dose optimization of these molecules are expected to advance, optimizing the benefit-risk profile for cancer patients.

Clinical Pharmacology Strategies for Bispecific Antibody Development: Learnings from FDA-Approved Bispecific Antibodies in Oncology.

Bispecific antibodies, by enabling the targeting of more than one disease-associated antigen or engaging immune effector cells, have both advantages and challenges compared with a combination of two different biological products. As of December 2023, there are 11 U.S. Food and Drug Administration-approved BsAb products on the market. Among these, 9 have been approved for oncology indications, and 8 of these are CD3 T-cell engagers. Clinical pharmacology strategies, including dose-related strategies, are critical for bispecific antibody development. This analysis reviewed clinical studies of all approved bispecific antibodies in oncology and identified dose-related perspectives to support clinical dose optimization and regulatory approvals, particularly in the context of the Food and Drug Administration's Project Optimus: (1) starting doses and dose ranges in first-in-human studies; (2) dose strategies including step-up doses or full doses for recommended phase 2 doses or dose level(s) used for registrational intent; (3) restarting therapy after dose delay; (4) considerations for the introduction of subcutaneous doses; (5) body weight vs. flat dosing strategy; and (6) management of immunogenicity. The learnings arising from this review are intended to inform successful strategies for future bispecific antibody development.

Using electronic health records for clinical pharmacology research: Challenges and considerations.

Electronic health records (EHRs) contain a vast array of phenotypic data on large numbers of individuals, often collected over decades. Due to the wealth of information, EHR data have emerged as a powerful resource to make first discoveries and identify disparities in our healthcare system. While the number of EHR-based studies has exploded in recent years, most of these studies are directed at associations with disease rather than pharmacotherapeutic outcomes, such as drug response or adverse drug reactions. This is largely due to challenges specific to deriving drug-related phenotypes from the EHR. There is great potential for EHR-based discovery in clinical pharmacology research, and there is a critical need to address specific challenges related to accurate and reproducible derivation of drug-related phenotypes from the EHR. This review provides a detailed evaluation of challenges and considerations for deriving drug-related data from EHRs. We provide an examination of EHR-based computable phenotypes and discuss cutting-edge approaches to map medication information for clinical pharmacology research, including medication-based computable phenotypes and natural language processing. We also discuss additional considerations such as data structure, heterogeneity and missing data, rare phenotypes, and diversity within the EHR. By further understanding the complexities associated with conducting clinical pharmacology research using EHR-based data, investigators will be better equipped to design thoughtful studies with more reproducible results. Progress in utilizing EHRs for clinical pharmacology research should lead to significant advances in our ability to understand differential drug response and predict adverse drug reactions.

Doses Evaluated in Clinical Pharmacology Studies Investigating the Effect of Intrinsic and Extrinsic Factors on PK and Safety: Case Examples from Approved Drug Development Programs.

Dose selection for investigations of intrinsic and extrinsic factors of pharmacokinetic variability as well as safety is a challenging question in the early clinical stage of drug development. The dose of an investigational product is chosen considering the compound information available to date, feasibility of the assessments, regulatory requirements, and the intent to maximize information for later regulatory submission. This review selected 37 programs as case examples of recently approved drugs to explore the doses selected with focus on studies of drug interaction, renal and hepatic impairment, food effect and concentration-QTc assessment.The review found that regulatory agencies may consider alternative approaches if justified and safe as illustrated in these examples. It is thus recommendable to use the first in human trial as an opportunity to assess QT-prolongation and drug interactions using probes or endogenous markers while maximizing the DDI potential, increasing sensitivity and ensuring safety. Early understanding of dose proportionality assists dose finding and simple and fast to conduct DDI study designs are advantageous. Single dose impairment studies despite non-proportional/time-dependent PK are often acceptability.Overall, the early understanding of the drug's safety profile is essential to ensure the safety of doses selected while preventing clinical trials with unnecessary exposure when using high doses or multiple doses. The information collected in this retrospective survey is a good reminder to tailor the early clinical program to the profile and needs of the molecule and consider regulatory opportunities to streamline the development path.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

Clinical Pharmacology Considerations for the "Off-the-Shelf" Allogeneic Cell Therapies.

Autologous chimeric antigen receptor T-cell (CAR-T) therapies have garnered unprecedented clinical success with multiple regulatory approvals for the treatment of various hematological malignancies. However, there are still several clinical challenges that limit their broad utilization for aggressive disease conditions. To address some of these challenges, allogeneic cell therapies are evaluated as an alternative approach. As compared with autologous products, they offer several advantages, such as a more standardized "off the shelf" product, reduced manufacturing complexity, and no requirement of bridging therapy. As with autologous CAR-T therapies, allogeneic cell therapies also present clinical pharmacology challenges due to their in vivo living nature, unique pharmacokinetics or cellular kinetics (CKs), and complex dose-exposure-response relationships that are impacted by various patient- and product-related factors. On top of that, allogeneic cell therapies present additional unique challenges, including attenuated in vivo persistence and graft-vs.-host disease risk as compared with autologous counterparts. This review draws comparison between autologous and allogeneic cell therapies, summarizing key engineering aspects unique to allogeneic cell therapy. Clinical pharmacology learnings from emerging clinical data of allogeneic cell therapy programs are also highlighted, with particular emphasis on CK, dose-exposure-response relationship, lymphodepletion regimen, repeat dosing, and patient- and product-related factors that can impact CK and patient outcomes. There are specific unique challenges and opportunities arising from the development of allogeneic cell therapies, especially in optimizing lymphodepletion and establishing a regimen for repeat dosing. This review highlights how clinical pharmacologists are well positioned to help address these challenges by leveraging novel clinical pharmacology and modeling and simulation approaches.

Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing.

BACKGROUND: Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have greatly benefitted from computational and mathematical advances over the past 60 years. Furthermore, the use of artificial intelligence (AI) and machine learning (ML) approaches for supporting clinical research and support is increasing. However, AI and ML applications for precision dosing have been evaluated only recently. Given the capability of ML to handle multidimensional data, such as from electronic health records, opportunities for AI and ML applications to facilitate TDM and MIPD may be advantageous. METHODS: This review summarizes relevant AI and ML approaches to support TDM and MIPD, with a specific focus on recent applications. The opportunities and challenges associated with this integration are also discussed. RESULTS: Various AI and ML applications have been evaluated for precision dosing, including those related to concentration or exposure prediction, dose optimization, population pharmacokinetics and pharmacodynamics, quantitative systems pharmacology, and MIPD system development and support. These applications provide an opportunity for ML and pharmacometrics to operate in an integrated manner to provide clinical decision support for precision dosing. CONCLUSIONS: Although the integration of AI with precision dosing is still in its early stages and is evolving, AI and ML have the potential to work harmoniously and synergistically with pharmacometric approaches to support TDM and MIPD. Because data are increasingly shared between institutions and clinical networks and aggregated into large databases, these applications will continue to grow. The successful implementation of these approaches will depend on cross-field collaborations among clinicians and experts in informatics, ML, pharmacometrics, clinical pharmacology, and TDM.

EXOGENOUS Sex Hormones and Sex Hormone Receptor Modulators in COVID-19: Rationale and Clinical Pharmacology Considerations.

Male patients with coronavirus disease 2019 (COVID-19) fare much worse than female patients in COVID-19 severity and mortality according to data from several studies. Because of this sex disparity, researchers hypothesize that the use of exogenous sex hormone therapy and sex hormone receptor modulators might provide therapeutic potential for patients with COVID-19. Repurposing approved drugs or drug candidates at late-stage clinical development could expedite COVID-19 therapy development because their clinical formulation, routes of administration, dosing regimen, clinical pharmacology, and potential adverse events have already been established or characterized in humans. A number of exogenous sex hormones and sex hormone receptor modulators are currently or will be under clinical investigation for COVID-19 therapy. In this review, we discuss the rationale for exogenous sex hormones and sex hormone receptor modulators in COVID-19 treatment, summarize ongoing and planned clinical trials, and discuss some of the clinical pharmacology considerations on clinical study design. To inform clinical study design and facilitate the clinical development of exogenous sex hormones and sex hormone receptor modulators for COVID-19 therapy, clinical investigators should pay attention to clinical pharmacology factors, such as dosing regimen, special populations (i.e., geriatrics, pregnancy, lactation, and renal/hepatic impairment), and drug interactions.

Use of Propensity Scoring and Its Application to Real-World Data: Advantages, Disadvantages, and Methodological Objectives Explained to Researchers Without Using Mathematical Equations.

Real-time data collection of patient health status and medications is sped up with modern electronic devices and technologies. As real-world data provide enormous research opportunities, propensity score (PS) methods have been getting attention due to their theoretical grounds in a nonrandomized study setting. In contrast to randomized clinical trials, observational clinical data obtained from a real-world database may not have balanced distributions of patient characteristics between treatment and control groups at the beginning of the respective study. These imbalanced distributions may cause a bias in an estimated treatment effect, which needs to be eliminated. Propensity scoring is one class of statistical methods to address the imbalance issue of real-world data sets. This article provides basic concepts and assesses advantages, disadvantages, and methodological objectives of propensity scoring. Targeting clinical pharmacology researchers with limited statistical background, 5 representative methods are reviewed and visualized: matching, stratification, covariate modeling, inverse probability of treatment weighting, and doubly robust methods. Examples of applications of PS methods were selected from the literature of outcomes research and drug development, nephrology, and pediatrics. Opportunities of applications related to these examples are described. Furthermore, potential future applications of PS methods in clinical pharmacology are discussed. The 21st Century Cures Act signed in 2016 encourages scientists to find opportunities to apply propensity scoring to real-world data. This article underscores that scientists need to justify their choice of statistical methods, whether a PS method or an alternative method, based on their clinical study design, statistical assumptions, and research objectives.

Model-Informed Drug Development Approaches to Assist New Drug Development in the COVID-19 Pandemic.

Leveraging limited clinical and nonclinical data through modeling approaches facilitates new drug development and regulatory decision making amid the coronavirus disease 2019 (COVID-19) pandemic. Model-informed drug development (MIDD) is an essential tool to integrate those data and generate evidence to (i) provide support for effectiveness in repurposed or new compounds to combat COVID-19 and dose selection when clinical data are lacking; (ii) assess efficacy under practical situations such as dose reduction to overcome supply issues or emergence of resistant variant strains; (iii) demonstrate applicability of MIDD for full extrapolation to adolescents and sometimes to young pediatric patients; and (iv) evaluate the appropriateness for prolonging a dosing interval to reduce the frequency of hospital visits during the pandemic. Ongoing research activities of MIDD reflect our continuous effort and commitment in bridging knowledge gaps that leads to the availability of effective treatments through innovation. Case examples are presented to illustrate how MIDD has been used in various stages of drug development and has the potential to inform regulatory decision making.

Sex-tailored pharmacology and COVID-19: Next steps towards appropriateness and health equity.

Making gender bias visible allows to fill the gaps in knowledge and understand health records and risks of women and men. The coronavirus disease 2019 (COVID-19) pandemic has shown a clear gender difference in health outcomes. The more severe symptoms and higher mortality in men as compared to women are likely due to sex and age differences in immune responses. Age-associated decline in sex steroid hormone levels may mediate proinflammatory reactions in older adults, thereby increasing their risk of adverse outcomes, whereas sex hormones and/or sex hormone receptor modulators may attenuate the inflammatory response and provide benefit to COVID-19 patients. While multiple pharmacological options including anticoagulants, glucocorticoids, antivirals, anti-inflammatory agents and traditional Chinese medicine preparations have been tested to treat COVID-19 patients with varied levels of evidence in terms of efficacy and safety, information on sex-targeted treatment strategies is currently limited. Women may have more benefit from COVID-19 vaccines than men, despite the occurrence of more frequent adverse effects, and long-term safety data with newly developed vectors are eagerly awaited. The prevalent inclusion of men in randomized clinical trials (RCTs) with subsequent extrapolation of results to women needs to be addressed, as reinforcing sex-neutral claims into COVID-19 research may insidiously lead to increased inequities in health care. The huge worldwide effort with over 3000 ongoing RCTs of pharmacological agents should focus on improving knowledge on sex, gender and age as pillars of individual variation in drug responses and enforce appropriateness.