Creating a Roadmap to Quantitative Systems Pharmacology-Informed Rare Disease Drug Development: A Workshop Report.

One of the goals of the Accelerating Rare Disease Cures (ARC) program in the Center for Drug Evaluation and Research (CDER) at the US Food and Drug Administration (FDA) is the development and use of regulatory and scientific tools, including drug/disease modeling, dose selection, and translational medicine tools. To facilitate achieving this goal, the FDA in collaboration with the University of Maryland Center of Excellence in Regulatory Science and Innovation (M-CERSI) hosted a virtual public workshop on May 11, 2023, entitled "Creating a Roadmap to Quantitative Systems Pharmacology-Informed Rare Disease Drug Development." This workshop engaged scientists from pharmaceutical companies, academic institutes, and the FDA to discuss the potential utility of quantitative systems pharmacology (QSP) in rare disease drug development and identify potential challenges and solutions to facilitate its use. Here, we report the main findings from this workshop, highlight the key takeaways, and propose a roadmap to facilitate the use of QSP in rare disease drug development.

Modeling Clinical Phenotype Variability: Consideration of Genomic Variations, Computational Methods, and Quantitative Proteomics.

Advances in biomedical and computer technologies have presented the modeling community the opportunity for mechanistically modeling and simulating the variability in a disease phenotype or in a drug response. The capability to quantify response variability can inform a drug development program. Quantitative systems pharmacology scientists have published various computational approaches for creating virtual patient populations (VPops) to model and simulate drug response variability. Genomic variations can impact disease characteristics and drug exposure and response. Quantitative proteomics technologies are increasingly used to facilitate drug discovery and development and inform patient care. Incorporating variations in genomics and quantitative proteomics may potentially inform creation of VPops to model and simulate virtual patient trials, and may help account for, in a predictive manner, phenotypic variations observed clinically.

Considerations for Use of Pharmacodynamic Biomarkers to Support Biosimilar Development - (I) A Randomized Trial with PCSK9 Inhibitors.

US Food and Drug Administration (FDA) guidance outlines how biosimilars can be developed based on pharmacokinetic (PK) and pharmacodynamic (PD) similarity study data in lieu of a comparative clinical efficacy study. There is a paucity of PD comparability studies in biosimilar development, leaving open questions about how best to plan these studies. To that end, we conducted a randomized, double-blinded, placebo-controlled, single-dose, parallel-arm clinical study in healthy participants to evaluate approaches to address information gaps, inform analysis best practices, and apply emerging technologies in biomarker characterization. Seventy-two healthy participants (n = 8 per arm) received either placebo or one of four doses of the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors alirocumab (15-100 mg) or evolocumab (21-140 mg) to evaluate the maximum change from baseline (ΔPDmax ) and the baseline-adjusted area under the effect curve (AUEC) for the biomarkers low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (apoB) in serum. We investigated approaches to minimize variability in PD measures. Coefficient of variation was lower for LDL-C than apoB at therapeutic doses. Modeling and simulation were used to establish the dose-response relationship and provided support that therapeutic doses for these products are adequately sensitive and are on the steep part of the dose-response curves. Similar dose-response relationships were observed for both biomarkers. ΔPDmax plateaued at lower doses than AUEC. In summary, this study illustrates how pilot study data can be leveraged to inform appropriate dosing and data analyses for a PK and PD similarity study.

Combating Viral Diseases in the Era of Systems Medicine.

Viruses can cause many diseases resulting in disabilities and death. Fortunately, advances in systems medicine enable the development of effective therapies for treating viral diseases, of vaccines to prevent viral infections, as well as of diagnostic tools to mitigate the risk and reduce the death toll. Characterizing the SARS-CoV-2 gene sequence and the role of its spike protein in infection informs development of small molecule drugs, antibodies, and vaccines to combat infection and complication, as well as to end the pandemic. Drug repurposing of small molecule drugs is a viable strategy to combat viral diseases; the key concepts include (1) linking a drug candidate's pharmacological network to its pharmacodynamic response in patients; (2) linking a drug candidate's physicochemical properties to its pharmacokinetic characteristics; and (3) optimizing the safe and effective dosing regimen within its therapeutic window. Computational integration of drug-induced signaling pathways with clinical outcomes is useful to inform selection of potential drug candidates with respect to safety and effectiveness. Key pharmacokinetic and pharmacodynamic principles for computational optimization of drug development include a drug candidate's Cminss/IC95 ratio, pharmacokinetic characteristics, and systemic exposure-response relationship, where Cminss is the trough concentration following multiple dosing. In summary, systems medicine approaches play a vital role in global success in combating viral diseases, including global real-time information sharing, development of test kits, drug repurposing, discovery and development of safe, effective therapies, detection of highly transmissible and deadly variants, and development of vaccines.

Quantitative systems pharmacology: Landscape analysis of regulatory submissions to the US Food and Drug Administration.

Quantitative systems pharmacology (QSP) has been proposed as a scientific domain that can enable efficient and informative drug development. During the past several years, there has been a notable increase in the number of regulatory submissions that contain QSP, including Investigational New Drug Applications (INDs), New Drug Applications (NDAs), and Biologics License Applications (BLAs) to the US Food and Drug Administration. However, there has been no comprehensive characterization of the nature of these regulatory submissions regarding model details and intended applications. To address this gap, a landscape analysis of all the QSP submissions as of December 2020 was conducted. This report summarizes the (1) yearly trend of submissions, (2) proportion of submissions between INDs and NDAs/BLAs, (3) percentage distribution along the stages of drug development, (4) percentage distribution across various therapeutic areas, and (5) nature of QSP applications. In brief, QSP is increasingly applied to model and simulate both drug effectiveness and safety throughout the drug development process across disease areas.

FDA-Industry Scientific Exchange on assessing quantitative systems pharmacology models in clinical drug development: a meeting report, summary of challenges/gaps, and future perspective.

The pharmaceutical industry is actively applying quantitative systems pharmacology (QSP) to make internal decisions and guide drug development. To facilitate the eventual development of a common framework for assessing the credibility of QSP models for clinical drug development, scientists from US Food and Drug Administration and the pharmaceutical industry organized a full-day virtual Scientific Exchange on July 1, 2020. An assessment form was used to ensure consistency in the evaluation process. Among the cases presented, QSP was applied to various therapeutic areas. Applications mostly focused on phase 2 dose selection. Model transparency, including details on expert knowledge and data used for model development, was identified as a major factor for robust model assessment. The case studies demonstrated some commonalities in the workflow of QSP model development, calibration, and validation but differ in the size, scope, and complexity of QSP models, in the acceptance criteria for model calibration and validation, and in the algorithms/approaches used for creating virtual patient populations. Though efforts are being made to build the credibility of QSP models and the confidence is increasing in applying QSP for internal decisions at the clinical stages of drug development, there are still many challenges facing QSP application to late stage drug development. The QSP community needs a strategic plan that includes the ability and flexibility to Adapt, to establish Common expectations for model Credibility needed to inform drug Labeling and patient care, and to AIM to achieve the goal (ACCLAIM).

History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications.

Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges.

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices.

Systems pharmacology approaches have the capability of quantitatively linking the key biological molecules relevant to a drug candidate's mechanism of action (drug-induced signaling pathways) to the clinical biomarkers associated with the proposed target disease, thereby quantitatively facilitating its development and life cycle management. In this review, the model attributes of published quantitative systems pharmacology (QSP) modeling for lowering cholesterol, treating salt-sensitive hypertension, and treating rare diseases as well as describing bone homeostasis and related pharmacological effects are critically reviewed with respect to model quality, calibration, validation, and performance. We further reviewed the common practices in optimizing QSP modeling and prediction. Notably, leveraging genetics and genomic studies for model calibration and validation is common. Statistical and quantitative assessment of QSP prediction and handling of model uncertainty are, however, mostly lacking as are the quantitative and statistical criteria for assessing QSP predictions and the covariance matrix of coefficients between the parameters in a validated virtual population. To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QSP modeling practices, which the scientific community supports, will advance QSP modeling in the realm of informed drug development. In the long run, good practices will extend the life cycles of QSP models beyond the life cycles of individual drugs.

A Computational Platform and Guide for Acceleration of Novel Medicines and Personalized Medicine.

In the era of big data and informatics, computational integration of data across the hierarchical structures of human biology enables discovery of new druggable targets of disease and new mode of action of a drug. We present herein a computational framework and guide of integrating drug targets, gene expression data, transcription factors, and prior knowledge of protein interactions to computationally construct the signaling network (mode of action) of a drug. In a similar manner, a disease network is constructed using its disease targets. And then, drug candidates are computationally prioritized by computationally ranking the closeness between a disease network and a drug's signaling network. Furthermore, we describe the use of the most perturbed HLA genes to assess the safety risk for immune-mediated adverse reactions such as Stevens-Johnson syndrome/toxic epidermal necrolysis.

Drug Repurposing for Ebola Virus Disease: Principles of Consideration and the Animal Rule.

There are no approved drugs or biologics to treat Ebola virus disease (EVD). Literature reviews identified a list of 141 drugs with reports of preliminary in vitro potency and in vivo effectiveness in animals or with reports of clinical use/trials in EVD patients. The majority of these drugs have been individually approved by the U.S. Food and Drug Administration for treating various non-EVD diseases. The anti-Ebola potency data of these drugs were curated from literature and publicly accessible databases, along with their individual biopharmaceutical and pharmacokinetic characteristics. To facilitate the development of antiviral drugs including anti-EVD drugs, highlights include optimization of the exposure-response relationship, design of a safe and effective clinical dosing regimen to achieve an adequate high ratio of clinical Cmin to a plasma protein binding-adjusted EC95, and the pharmacokinetic studies needed in animal models (healthy and affected) and in healthy volunteers. The exposure/response relationship for human dose selection is summarized, as described in the U.S. Food and Drug Administration "Animal Rule'' guidance when human efficacy studies are not ethical or feasible.

Translational systems pharmacology-based predictive assessment of drug-induced cardiomyopathy.

Drug-induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug's signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs' signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP-augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave-one-out cross validation. The ILP-constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin-induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers.

A Biologically-Based Computational Approach to Drug Repurposing for Anthrax Infection.

Developing drugs to treat the toxic effects of lethal toxin (LT) and edema toxin (ET) produced by B. anthracis is of global interest. We utilized a computational approach to score 474 drugs/compounds for their ability to reverse the toxic effects of anthrax toxins. For each toxin or drug/compound, we constructed an activity network by using its differentially expressed genes, molecular targets, and protein interactions. Gene expression profiles of drugs were obtained from the Connectivity Map and those of anthrax toxins in human alveolar macrophages were obtained from the Gene Expression Omnibus. Drug rankings were based on the ability of a drug/compound's mode of action in the form of a signaling network to reverse the effects of anthrax toxins; literature reports were used to verify the top 10 and bottom 10 drugs/compounds identified. Simvastatin and bepridil with reported in vitro potency for protecting cells from LT and ET toxicities were computationally ranked fourth and eighth. The other top 10 drugs were fenofibrate, dihydroergotamine, cotinine, amantadine, mephenytoin, sotalol, ifosfamide, and mefloquine; literature mining revealed their potential protective effects from LT and ET toxicities. These drugs are worthy of investigation for their therapeutic benefits and might be used in combination with antibiotics for treating B. anthracis infection.

Pharmacodynamics and Systems Pharmacology Approaches to Repurposing Drugs in the Wake of Global Health Burden.

There are emergent needs for cost-effective treatment worldwide, for which repurposing to develop a drug with existing marketing approval of disease(s) for new disease(s) is a valid option. Although strategic mining of electronic health records has produced real-world evidences to inform drug repurposing, using omics data (drug and disease), knowledge base of protein interactions, and database of transcription factors have been explored. Structured integration of all the existing data under the framework of drug repurposing will facilitate decision making. The ability to foresee the need to integrate new data types produced by emergent technologies and to enable data connectivity in the context of human biology and targeted diseases, as well as to use the existing crucial quality data of all approved drugs will catapult the number of drugs being successfully repurposed. However, translational pharmacodynamics databases for modeling information across human biology in the context of host factors are lacking and are critically needed for drug repurposing to improve global public health, especially for the efforts to combat neglected tropic diseases as well as emergent infectious diseases such as Zika or Ebola virus.

Trastuzumab-Related Cardiotoxic Effects in Taiwanese Women: A Nationwide Cohort Study.

IMPORTANCE: Trastuzumab is an essential medicine per the World Health Organization Model List, but its cardiac safety information in Asian women is limited. OBJECTIVE: To estimate the rate and the risk of heart failure (HF) and/or cardiomyopathy (CM) in Asian women undergoing trastuzumab treatment. DESIGN: This cohort study used the Taiwanese National Health Insurance Research Database (NHIRD), a nationwide claim database covering more than 99% of the entire Taiwanese population, to identify 23 006 women with incident breast cancer (BC) who received chemotherapy from 2006 to 2009. We grouped women per their initial treatment regimens and found 1066 new trastuzumab users. We matched trastuzumab users with nonusers by year of BC diagnosis and propensity score (PS) with the caliper widths at 0.25 standard deviation of PS (up to 4 nonusers per trastuzumab user). The study lasted from January 2006 to December 2013 with a median follow-up of 5.29 years and a landmark design to avoid immortal time bias. EXPOSURE: Trastuzumab. MAIN OUTCOMES AND MEASURES: To estimate HF and/or CM rates and time to HF and/or CM, we employed a cause-specific hazard model. Trastuzumab exposure was a time-dependent variable, while cumulative courses of chemotherapy agents with known cardiotoxic effects (including anthracyclines, taxanes, and cyclophosphamide) were defined as time-dependent covariates in the analysis model. We also performed 6 sensitivity analyses. RESULTS: In this cohort of 23 006 women (mean age, 50.99 years), the crude incidence of HF and/or CM was 4.03% in trastuzumab users and 2.88% in nonusers. The median time to HF and/or CM was 456 days in trastuzumab users and 966 days in nonusers. The 1-year cumulative hazard ratio was 1.86 (95% CI, 1.08-3.19). The sensitivity analyses yielded similar results. CONCLUSIONS AND RELEVANCE: Compared with the published results, the trastuzumab-related HF and/or CM rate was 5-fold lower in Taiwanese women with breast cancer. Nonetheless, our cohort had a similar trastuzumab-related HF and/or CM risk. Our study provides critical cardiac safety information of trastuzumab for Asian women with BC under current treatment guidelines and label information.