In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Designing Clinical Trials for Patients With Rare Cancers: Connecting the Zebras

The field of rare cancer research is rapidly transforming, marked by significant progress in clinical trials and treatment strategies. Rare cancers, as defined by the National Cancer Institute, occur in fewer than 150 cases per million people each year, yet they collectively represent a significant portion of all cancer diagnoses. Because of their infrequency, these cancers pose distinct challenges for clinical trials, including limited patient populations, geographical dispersion, and a general lack of awareness of treatment options. Economic limitations further complicate drug development, making initiatives such as the Orphan Drug Act essential for incentivizing research. The advent of next-generation sequencing (NGS) and precision medicine has been instrumental in identifying actionable genetic alterations in parallel with an explosion in the development of genomically targeted therapies, immunotherapies, and antibody drug conjugates. Advances in clinical NGS, precision medicine, and tumor-agnostic therapies have become central to the progress in rare cancer research. The development and approval of tumor-agnostic drugs, such as BRAF, NTRK, and RET inhibitors, and immunotherapy for mismatch repair deficient/microsatellite instability-high status cancers highlight the potential of personalized treatments across diverse cancer types and across the age spectrum. Collaborative trials from cooperative groups including SWOG DART, ASCO TAPUR, NCI-MATCH, pediatric COG-match, DRUP, IMPRESS, and innovative registrational basket and platform trials (eg, VE-Basket, ROAR, LIBRETTO-001, ARROW), along with patient advocacy group-run trials like TRACK, are enhancing access to clinical trials. In addition, artificial intelligence has the potential to improve the trial matching process. An integrated approach, combining these innovations in collaboration with multiple stakeholders, is crucial for advancing rare cancer research, offering hope for better patient outcomes and quality of life.

A Model of Intratumor and Interpatient Heterogeneity Explains Clinical Trials of Curative Combination Therapy for Lymphoma

SIGNIFICANCE

A new model of intratumor and interpatient heterogeneity in response to drug combinations explains and predicts the results of clinical trials of curative-intent treatments for DLBCL. This model can be used to understand and inform optimal design of curative drug combinations and clinical trials. See related commentary by Goldstein et al., p. 153.

Phytochemical synergies in BK002: advanced molecular docking insights for targeted prostate cancer therapy

Achyranthes japonica (Miq.) Nakai (AJN) and Melandrium firmum (Siebold and Zucc.) Rohrb. (MFR) are medicinal plants recognized for their bioactive phytochemicals, including ecdysteroids, anthraquinones, and flavonoids. This study investigates the anticancer properties of key constituents of these plants, focusing on the BK002 formulation, a novel combination of AJN and MFR. Specifically, the research employs advanced molecular docking and in silico analyses to assess the interactions of bioactive compounds ecdysterone, inokosterone, and 20-hydroxyecdysone (20-HE) with key prostate cancer-related network proteins, including 5α-reductase, CYP17, DNMT1, Dicer, PD-1, and PD-L1. Molecular docking techniques were applied to evaluate the binding affinities contributions of the bioactive compounds in BK002 against prostate cancer-hub network targets. The primary focus was on enzymes like 5α-reductase and CYP17, which are central to androgen biosynthesis, as well as on cancer-related proteins such as DNA methyltransferase 1 (DNMT1), Dicer, programmed death-1 (PD-1), and programmed death ligand-1 (PD-L1). Based on data from prostate cancer patients, key target networks were identified, followed by in silico analysis of the primary bioactive components of BK002.In silico assessments were conducted to evaluate the safety profiles of these compounds, providing insights into their therapeutic potential. The docking studies revealed that ecdysterone, inokosterone, and 20-hydroxyecdysonec demonstrated strong binding affinities to the critical prostate cancer-related enzymes 5α-reductase and CYP17, contributing to a potential reduction in androgenic activity. These compounds also exhibited significant inhibitory interactions with DNMT1, Dicer, PD-1, and PD-L1, suggesting a capacity to interfere with key oncogenic and immune evasion pathways. Ecdysterone, inokosterone, and 20-hydroxyecdysone have demonstrated the ability to target key oncogenic pathways, and their favorable binding affinity profiles further underscore their potential as novel therapeutic agents for prostate cancer. These findings provide a strong rationale for further preclinical and clinical investigations, supporting the integration of BK002 into therapeutic regimens aimed at modulating tumor progression and immune responses.

Harnessing the AI/ML in Drug and Biological Products Discovery and Development: The Regulatory Perspective

Artificial Intelligence (AI) has the disruptive potential to transform patients' lives via innovations in pharmaceutical sciences, drug development, clinical trials, and manufacturing. However, it presents significant challenges, ethical concerns, and risks across sectors and societies. AI's rapid advancement has revealed regulatory gaps as existing public policies struggle to keep pace with the challenges posed by these emerging technologies. The term AI itself has become commonplace to argue that greater "human oversight" for "machine intelligence" is needed to harness the power of this revolutionary technology for both potential and risk management, and hence to call for more practical regulatory guidelines, harmonized frameworks, and effective policies to ensure safety, scalability, data privacy, and governance, transparency, and equitable treatment. In this review paper, we employ a holistic multidisciplinary lens to survey the current regulatory landscape with a synopsis of the FDA workshop perspectives on the use of AI in drug and biological product development. We discuss the promises of responsible data-driven AI, challenges and related practices adopted to overcome limitations, and our practical reflections on regulatory oversight. Finally, the paper outlines a path forward and future opportunities for lawful ethical AI. This review highlights the importance of risk-based regulatory oversight, including diverging regulatory views in the field, in reaching a consensus.

The PERMIT guidelines for designing and implementing all stages of personalised medicine research

Personalised medicine (PM) research programmes represent the modern paradigm of complex cross-disciplinary research, integrating innovative methodologies and technologies. Methodological research is required to ensure that these programmes generate robust and reproducible evidence. The PERMIT project developed methodological recommendations for each stage of the PM research pipeline. A common methodology was applied to develop the recommendations in collaboration with relevant stakeholders. Each stage was addressed by a dedicated working group, specializing in the subject matter. A series of scoping reviews that mapped the methods used in PM research and a gap analysis were followed by working sessions and workshops where field experts analyzed the gaps and developed recommendations. Through collaborative writing and consensus building exercises, the final recommendations were defined. They provide guidance for the design, implementation and evaluation of PM research, from patient and omics data collection and sample size calculation to the selection of the most appropriate stratification approach, including machine learning modeling, the development and application of reliable preclinical models, and the selection and implementation of the most appropriate clinical trial design. The dissemination and implementation of these recommendations by all stakeholders can improve the quality of PM research, enhance the robustness of evidence, and improve patient care.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable but is hindered by the limited performance of existing biomarkers. Here, we leveraged in silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We evaluated and quantified the performance of 90 biomarker candidates, including various cellular and molecular species, at different cutoffs by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pretreatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Assessing the Role of Patient Generation Techniques in Virtual Clinical Trial Outcomes

Virtual clinical trials (VCTs) are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. In the context of a VCT, a plausible patient is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. A number of techniques have been introduced to determine the set of model parametrizations to include in a virtual patient cohort. These methodologies generally start with a prior distribution for each model parameter and utilize some criteria to determine whether a parameter set sampled from the priors should be included or excluded from the plausible population. No standard technique exists, however, for generating these prior distributions and choosing the inclusion/exclusion criteria. In this work, we rigorously quantify the impact that VCT design choices have on VCT predictions. Rather than use real data and a complex mathematical model, a spatial model of radiotherapy is used to generate simulated patient data and the mathematical model used to describe the patient data is a two-parameter ordinary differential equations model. This controlled setup allows us to isolate the impact of both the prior distribution and the inclusion/exclusion criteria on both the heterogeneity of plausible populations and on predicted treatment response. We find that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the plausible population. Yet, the percent of treatment responders in the plausible population was more sensitive to the inclusion/exclusion criteria utilized. This foundational understanding of the role of virtual clinical trial design should help inform the development of future VCTs that use more complex models and real data.

Critical Appraisal and Future Challenges of Artificial Intelligence and Anticancer Drug Development

The conventional rules for anti-cancer drug development are no longer sufficient given the relatively limited number of patients available for therapeutic trials. It is thus a real challenge to better design trials in the context of new drug approval for anti-cancer treatment. Artificial intelligence (AI)-based in silico trials can incorporate far fewer but more informative patients and could be conducted faster and at a lower cost. AI can be integrated into in silico clinical trials to improve data analysis, modeling and simulation, personalized medicine approaches, trial design optimization, and virtual patient generation. Health authorities are encouraged to thoroughly review the rules for setting up clinical trials, incorporating AI and in silico methodology once they have been appropriately validated. This article also aims to highlight the limits and challenges related to AI and machine learning.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable, but is hindered by the limited performance of existing biomarkers. Here, we leveraged in-silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We tested 90 biomarker candidates, including various cellular and molecular species, by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pre-treatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Surrogacy and the Valuation of ATMPs: Taking Our Place in the Evidence Generation/Assessment Continuum

Medical technology is advancing rapidly, but established methods for health technology assessment are struggling to keep up. This challenge is particularly stark for the assessment of advanced therapy medicinal products-therapies often launched on the basis of single-arm studies powered to a surrogate primary endpoint. The most robust surrogacy methods investigate trial-level correlations between the treatment effect on the surrogate and the outcome of ultimate interest. However, these methods are often impossible with the evidence usually available for advanced therapy medicinal products at the time of the launch (randomized controlled trials are necessary for these advanced methods). Additionally, these surrogacy relationships are usually considered to be technology specific, adding uncertainty for any approach that primarily relies on historic data to estimate the surrogacy relationship for novel interventions such as advanced therapy medicinal products. The literature has already highlighted the need for early dialogue, staged assessment processes, and pricing arrangements that responsibly share the risk between the manufacturer and payer. However, it is our view that in addition to these critical developments, the modeling methods employed could also improve. Currently, health technology assessment practitioners typically either ignore the surrogate and simply extrapolate the endpoint of greatest patient relevance irrespective of the degree of maturity or assume historic surrogate relationships apply to the novel technology. In this opinion piece, we outline an additional avenue. By drawing on the understanding of the mechanism of action and insights generated earlier in the evidence generation/assessment continuum, cost-effectiveness modelers can make better use of the wider data available. These efforts are expected to reduce uncertainty at the time of the initial launch of pharmaceutical products and increase the value of subsequent data collection efforts.

Perspective: rethinking therapeutic strategies in oncology

Immuno-oncology has revolutionized cancer care, drug development, the design of clinical trials, standard treatment paradigms, and the evaluation of response to therapy. These are all areas, however, that have not fully incorporated principles of tumor immunology. Insufficient emphasis is put on the effect drugs have on the immune system, and specifically, the impact that multiple lines of therapy can have on the functioning of the immune system, hindering a robust anti-tumor immune response. A paradigm shift in how we approach the development of novel immunotherapeutic agents is necessary to facilitate the effective improvements in patient outcomes.

Development of bispecific T cell engagers: harnessing quantitative systems pharmacology

Bispecific T cell engagers (bsTCEs) have emerged as a promising class of cancer immunotherapy. Several bsTCEs have achieved marketing approval; dozens more are under clinical investigation. However, the clinical development of bsTCEs remains rife with challenges, including nuanced pharmacology, limited translatability of preclinical findings, frequent on-target toxicity, and convoluted dosing regimens. In this opinion article we present a distinct perspective on how quantitative systems pharmacology (QSP) can serve as a powerful tool for overcoming these obstacles. Recent advances in QSP modeling have empowered developers of bsTCEs to gain a deeper understanding of their context-dependent pharmacology, bridge gaps in experimental data, guide first-in-human (FIH) dose selection, design dosing regimens with expanded therapeutic windows, and improve long-term treatment outcomes. We use recent case studies to exemplify the potential of QSP techniques to support future bsTCE development.

Mathematical Modeling Support for Lung Cancer Therapy-A Short Review

The paper presents a review of models that can be used to describe dynamics of lung cancer growth and its response to treatment at both cell population and intracellular processes levels. To address the latter, models of signaling pathways associated with cellular responses to treatment are overviewed. First, treatment options for lung cancer are discussed, and main signaling pathways and regulatory networks are briefly reviewed. Then, approaches used to model specific therapies are discussed. Following that, models of intracellular processes that are crucial in responses to therapies are presented. The paper is concluded with a discussion of the applicability of the presented approaches in the context of lung cancer.