Asia-Inclusive Clinical Research and Development Enabled by Translational Science and Quantitative Clinical Pharmacology: Toward a Culture That Challenges the Status Quo

Asia-Inclusive Global Development of Enpatoran: Results of an Ethno-Bridging Study, Intrinsic/Extrinsic Factor Assessments and Disease Trajectory Modeling to Inform Design of a Phase II Multiregional Clinical Trial

Enpatoran is a novel, highly selective, and potent dual toll-like receptor (TLR)7 and TLR8 inhibitor currently under development for the treatment of autoimmune disorders including systemic lupus erythematosus (SLE), cutaneous lupus erythematosus (CLE), and myositis. The ongoing phase II study (WILLOW; NCT05162586) is evaluating enpatoran for 24 weeks in patients with active SLE or CLE and is currently recruiting. To support development of WILLOW as an Asia-inclusive multiregional clinical trial (MRCT) according to International Conference on Harmonisation E5 and E17 principles, we have evaluated ethnic sensitivity to enpatoran based on clinical pharmacokinetic (PK), pharmacodynamic (PD), and safety data from an ethno-bridging study (NCT04880213), supplemented by relevant quantitative PK, PD, and disease trajectory modeling (DTM) results, and drug metabolism/disease knowledge. A single-center, open-label, sequential dose group study in White and Japanese subjects matched by body weight, height, and sex demonstrated comparable PK and PD properties for enpatoran in Asian vs. non-Asian (White and other) subjects across single 100, 200, and 300 mg orally administered doses. DTM suggested no significant differences in SLE disease trajectory for Asian vs. non-Asian individuals. Aldehyde oxidase (AOX) is considered to be a key contributor to enpatoran metabolism, and a literature review indicated no relevant ethnic differences in AOX function based on in vitro and clinical PK data from marketed drugs metabolized by AOX, supporting the conclusion of low ethnic sensitivity for enpatoran. Taken together, the inclusion of Asian patients in MRCTs including WILLOW was informed based on a Totality of Evidence approach.

An Investigation in the Comparability of the Exposure and Recommended Dose of Selected Pfizer Drugs in East Asian Countries: Is Mutual Usage of Clinical Data Among East Asian Countries Feasible?

The current regulatory path for new drug registration in East Asian countries has led to significant delay of the new medicines in these countries. A unified regulatory path and allowance of mutual usage of clinical data in East Asian countries would lead to cost saving in drug development and expedite the new drug registration in these countries. The objectives of the present analysis are to compare the approval dates of a selection of products developed by Pfizer in the United States and East Asian countries (China, Japan, Korea) and compare the pharmacokinetics and recommended doses of these products in East Asian countries. Eighteen products (20 drugs, 2 products with 2 combination drugs) with exposure data available in at least 2 of the 3 East Asian countries across different therapeutic areas were included in the analyses. The results showed that most products had delayed approval in East Asian countries (up to 8 years) after US or EU approval. No distinct differences were observed in the drug exposure and recommended doses for the selected products in East Asian countries. These results together with literature data of genetic similarity of the East Asian populations support the mutual usage of the clinical data in the East Asian countries for expedited regulatory submission and approval.

Machine Learning in Modeling Disease Trajectory and Treatment Outcomes: An Emerging Enabler for Model-Informed Precision Medicine

The increasing breadth and depth of resolution in biological and clinical data, including -omics and real-world data, requires advanced analytical techniques like artificial intelligence (AI) and machine learning (ML) to fully appreciate the impact of multi-dimensional population variability in intrinsic and extrinsic factors on disease progression and treatment outcomes. Integration of advanced data analytics in Quantitative Pharmacology is crucial for drug-disease knowledge management, enabling precise, efficient and inclusive drug development and utilization - an application we refer to as model-informed precision medicine. AI/ML enables characterization of the molecular and clinical sources of heterogeneity in disease trajectory, advancing end point qualification and biomarker discovery, and informing patient enrichment for proof-of-concept studies as well as trial designs for efficient evidence generation incorporating digital twins and virtual control arms. Explainable ML methods are valuable in elucidating predictors of efficacy and safety of pharmacological treatments, thereby informing response monitoring and risk mitigation strategies. In oncology, emerging opportunities exist for development of the next generation of disease models via ML-assisted joint longitudinal modeling of high-dimensional biomarker data such as circulating tumor DNA and radiomics profiles as predictors of survival outcomes. Finally, mining real-world data leveraging ML algorithms enables understanding of the impact of exclusion criteria on clinical outcomes, thereby informing rational design of appropriately inclusive clinical trials through data-driven broadening of eligibility criteria. Herein, we provide an overview of the aforementioned contexts of use of ML in drug-disease modeling based on examples across multiple therapeutic areas including neurology, rare diseases, autoimmune diseases, oncology and immuno-oncology.

Assessing Ethnic Minority Representation in Fibromyalgia Clinical Trials: A Systematic Review of Recruitment Demographics

The under-representation of non-White participants in Western countries in clinical research has received increased attention, due to recognized physiological differences between ethnic groups, which may affect the efficacy and optimal dosage of some treatments. This review assessed ethnic diversity in pharmaceutical trials for fibromyalgia, a poorly understood chronic pain disorder. We also investigated longitudinal change to non-White participant proportions in trials and non-White participants' likelihood to discontinue with fibromyalgia research between trial stages (retention). First, we identified relevant trials conducted in the United States and Canada between 2000 and 2022, by searching PubMed, Web of Science, Scopus, and the Cochrane Library databases. In trials conducted both across the United States and Canada, and exclusively within the United States, approximately 90% of participants were White. A longitudinal analysis also found no change in the proportion of non-White participants in trials conducted across the United States and Canada between 2000 and 2022. Finally, we found no significant differences in trial retention between White and non-White participants. This review highlights the low numbers of ethnic minorities in fibromyalgia trials conducted in the United States and Canada, with no change to these proportions over the past 22 years. Furthermore, non-White participants were not more likely to discontinue with the fibromyalgia research once they were recruited.

Characteristics of Asian Participation in Multi-regional Clinical Trials Reviewed for Drug Approval in Japan: Opportunities for Collaboration Between South-East Asia, East Asia, and Japan

Although the percentage of multi-regional clinical trials (MRCTs) submitted for drug approval in Japan increased significantly since the 2007 publication of the regulatory guideline, "Basic principles on global clinical trials", strategic collaborations between Asian countries will be important to promote MRCTs in accordance with the ICH E17 guideline published in 2017. In this study, characteristics of MRCTs reviewed for drug approval in Japan, especially those with participation by South-East Asia and East Asia, were investigated to explore opportunities for collaborations on global drug development in Asia. More than 90% of reviewed trials were conducted as global MRCTs. In addition to Japan, South-East Asia has participated in various types of MRCTs in terms of total numbers of subjects and countries. However, South-East Asia participation was lower in large-size MRCTs (total sample size ≥ 1000) than in middle- (500 ≤ total sample size < 1000) and small-size MRCTs (total sample size < 500). Furthermore, similar clinical trials for the same indications to the MRCTs without South-East Asia were rarely conducted separately in South-East Asia. Participation of other Asian countries did not affect the percentage of Japanese subjects enrolled in an MRCT, but did significantly increase the percentage of participating Asian subjects. These results suggest that additional opportunities for collaboration on MRCTs may be possible between Japan and other Asian countries, especially more collaborations with South-East Asia in the large-size MRCTs. More data of Asian populations from MRCTs will be useful for exploring an important ethnic factor affecting drug response, and will provide a sound scientific basis in considering the application of the pooled data concept in Asia, as described in the ICH E17 guideline.

Interethnic differences in drug response: projected impact of genetic variations in the Saudi population

Ethnicity is known to have an impact on drug responses. This is particularly important for drugs that have a narrow therapeutic window, nonlinearity in pharmacokinetics and are metabolized by enzymes that demonstrate genetic polymorphisms. However, most clinical trials are conducted among Caucasians, which might limit the usefulness of the findings of such studies for other ethnicities. The representation of participants from Saudi Arabia in global clinical trials is low. Therefore, there is a paucity of evidence to assess the impact of ethnic variability in the Saudi population on drug response. In this article, the authors assess the projected impact of genetic polymorphisms in drug-metabolizing enzymes and drug targets on drug response in the Saudi population.

Disease trajectory of SLE clinical endpoints and covariates affecting disease severity and probability of response: Analysis of pooled patient-level placebo (Standard-of-Care) data to enable model-informed drug development

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple organ systems. Many investigational agents have failed or shown only modest effects when added to standard of care (SoC) therapy in placebo-controlled trials, and only two therapies have been approved for SLE in the last 60 years. Clinical trial outcomes have shown discordance in drug effects between clinical endpoints. Herein, we characterized longitudinal disease activity in the SLE population and the sources of variability by developing a latent disease trajectory model for SLE component endpoints (Systemic Lupus Erythematosus Disease Activity Index [SLEDAI], Physician's Global Assessment [PGA], British Isles Lupus Assessment Group Index [BILAG]) and composite endpoints (Systemic Lupus Erythematosus Responder Index [SRI], BILAG-based Composite Lupus Assessment [BICLA], and Lupus Low Disease Activity State [LLDAS]) using patient-level historical SoC data from nine phase II and III studies. Across all endpoints, in predictions up to 52 weeks from the final disease trajectory model, the following baseline covariates were associated with a greater decrease in SLE disease activity and higher response to placebo + SoC: Hispanic ethnicity from Central/South America, absence of hypocomplementemia, recent SLE diagnosis, and high baseline disease activity score using SLEDAI and BILAG separately. No discernible differences were observed in the trajectory of response to placebo + SoC across different SoC medications (antimalarial and immunosuppressant such as mycophenolate, methotrexate, and azathioprine). Across all endpoints, disease trajectory showed no difference in Asian versus non-Asian patients, supporting Asia-inclusive global SLE drug development. These results describe the first population approach to support a model-informed drug development framework in SLE.