Afamitresgene autoleucel for advanced synovial sarcoma and myxoid round cell liposarcoma (SPEARHEAD-1): an international, open-label, phase 2 trial.

BACKGROUND: Afamitresgene autoleucel (afami-cel) showed acceptable safety and promising efficacy in a phase 1 trial (NCT03132922). The aim of this study was to further evaluate the efficacy of afami-cel for the treatment of patients with HLA-A*02 and MAGE-A4-expressing advanced synovial sarcoma or myxoid round cell liposarcoma. METHODS: SPEARHEAD-1 was an open-label, non-randomised, phase 2 trial done across 23 sites in Canada, the USA, and Europe. The trial included three cohorts, of which the main investigational cohort (cohort 1) is reported here. Cohort 1 included patients with HLA-A*02, aged 16-75 years, with metastatic or unresectable synovial sarcoma or myxoid round cell liposarcoma (confirmed by cytogenetics) expressing MAGE-A4, and who had received at least one previous line of anthracycline-containing or ifosfamide-containing chemotherapy. Patients received a single intravenous dose of afami-cel (transduced dose range 1·0 × 109-10·0 × 109 T cells) after lymphodepletion. The primary endpoint was overall response rate in cohort 1, assessed by a masked independent review committee using Response Evaluation Criteria in Solid Tumours (version 1.1) in the modified intention-to-treat population (all patients who received afami-cel). Adverse events, including those of special interest (cytokine release syndrome, prolonged cytopenia, and neurotoxicity), were monitored and are reported for the modified intention-to-treat population. This trial is registered at ClinicalTrials.gov, NCT04044768; recruitment is closed and follow-up is ongoing for cohorts 1 and 2, and recruitment is open for cohort 3. FINDINGS: Between Dec 17, 2019, and July 27, 2021, 52 patients with cytogenetically confirmed synovial sarcoma (n=44) and myxoid round cell liposarcoma (n=8) were enrolled and received afami-cel in cohort 1. Patients were heavily pre-treated (median three [IQR two to four] previous lines of systemic therapy). Median follow-up time was 32·6 months (IQR 29·4-36·1). Overall response rate was 37% (19 of 52; 95% CI 24-51) overall, 39% (17 of 44; 24-55) for patients with synovial sarcoma, and 25% (two of eight; 3-65) for patients with myxoid round cell liposarcoma. Cytokine release syndrome occurred in 37 (71%) of 52 of patients (one grade 3 event). Cytopenias were the most common grade 3 or worse adverse events (lymphopenia in 50 [96%], neutropenia 44 [85%], leukopenia 42 [81%] of 52 patients). No treatment-related deaths occurred. INTERPRETATION: Afami-cel treatment resulted in durable responses in heavily pre-treated patients with HLA-A*02 and MAGE-A4-expressing synovial sarcoma. This study shows that T-cell receptor therapy can be used to effectively target solid tumours and provides rationale to expand this approach to other solid malignancies. FUNDING: Adaptimmune.

Systems pharmacology to predict drug toxicity: integration across levels of biological organization.

To achieve sensitive and specific mechanism-based prediction of drug toxicity, the tools of systems pharmacology will be integrated using structured ontological approaches, analytics, mathematics, and statistics. Success of this effort is based on the assumption that a systems network that consists of drug-induced perturbations of physiological functions can be characterized. This network spans the hierarchy of biological organization, from gene to mRNA to protein to intracellular organelle to cell to organ to organism. It is populated with data from each of these levels of biological organization. These data, from disparate sources, include the published literature, drug development archives of all approved drugs and drug candidates that did not complete development, and various toxicity databases and adverse event reporting systems. The network contains interrelated genomics, transcriptomics, and metabolomics data, as well as organ and physiological functional data that are derived from the universe of information that describes and analyzes drug toxicity. Here we describe advances in bioinformatics, computer sciences, next-generation sequencing, and systems biology that create the opportunity for integrated systems pharmacology-based prediction of drug safety.

Computed Biological Relations among Five Select Treatment-Related Organ/Tissue Toxicities.

Drug toxicity presents a major challenge in drug development and patient care. We set to build upon previous works regarding select drug-induced toxicities to find common patterns in the mode of action of the drugs associated with these toxicities. In particular, we focused on five disparate organ toxicities, peripheral neuropathy (PN), rhabdomyolysis (RM), Stevens-Johnson syndrome/toxic epidermal necrosis (SJS/TEN), lung injury (LI), and heart contraction-related cardiotoxicity (CT), and identified biological commonalities between and among the toxicities in terms of pharmacological targets and nearest neighbors (indirect effects) using the hyper-geometric test and a distance metric of Spearman correlation. There were 20 significant protein targets associated with two toxicities and 0 protein targets associated with three or more toxicities. Per Spearman distance, PN was closest to SJS/TEN compared to other pairs, whereas the pairs involving RM were more different than others excluding RM. The significant targets associated with RM outnumbered those associated with every one of the other four toxicities. Enrichment analysis of drug targets that are expressed in corresponding organ/tissues determined proteins that should be avoided in drug discovery. The identified biological patterns emerging from the mode of action of these drugs are statistically associated with these serious toxicities and could potentially be used as predictors for new drug candidates. The predictive power and usefulness of these biological patterns will increase with the database of these five toxicities. Furthermore, extension of our approach to all severe adverse reactions will produce useful biological commonalities for reference in drug discovery and development.

Systems pharmacological analysis of drugs inducing stevens-johnson syndrome and toxic epidermal necrolysis.

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are serious cutaneous adverse reactions. We mined the approved labels in Drugs@FDA, identified the SJS/TEN list of 259 small molecular drugs and biologics, and conducted systems pharmacological network analyses. Pharmacological network analysis revealed that drugs with treatment-related SJS and/or TEN are pharmacologically diverse and that the largest subnetwork is associated with antiepileptic drugs and their pharmacological targets. Our pharmacological network analysis identified CTNNB1 [catenin (cadherin-associated protein), beta 1, 88 kDa] as a significant intermediator. This protein is involved in maintaining the functional integrity of the epithelium through regulating cell growth and adhesion between cells in various organs, including the skin. Leveraging a publicly accessible genome-wide transcriptional expression database, we found that human leukocyte antigen-related (HLA) genes were significantly perturbed by various SJS/TEN-inducing drugs. Notably, carbamazepine (CBZ) perturbed several HLA genes, among which HLA-DQB1*0201 was reportedly shown to be associated with CBZ-induced SJS/TEN in caucasians. In short, systems analysis by leveraging a publicly accessible knowledge base and databases could produce meaningful results for further mechanistic investigation. Our study sheds light on the utility of systems pharmacology analysis for gaining insight into clinical drug toxicity.

Drug-induced rhabdomyolysis: from systems pharmacology analysis to biochemical flux.

The goal of this study was to integrate systems pharmacology and biochemical flux to delineate drug-induced rhabdomyolysis by leveraging prior knowledge and publicly accessible data. A list of 211 rhabdomyolysis-inducing drugs (RIDs) was compiled and curated from multiple sources. Extended pharmacological network analysis revealed that the intermediators directly interacting with the pharmacological targets of RIDs were significantly enriched with functions such as regulation of cell cycle, apoptosis, and ubiquitin-mediated proteolysis. A total of 78 intermediators were shown to be significantly connected to at least five RIDs, including estrogen receptor 1 (ESR1), synuclein gamma (SNCG), and janus kinase 2 (JAK2). Transcriptomic analysis of RIDs profiled in Connectivity Map on the global scale revealed that multiple pathways are perturbed by RIDs, including ErbB signaling and lipid metabolism pathways, and that carnitine palmitoyl transferase 2 (CPT2) was in the top 1 percent of the most differentially perturbed genes. CPT2 was downregulated by nine drugs that perturbed the genes significantly enriched in oxidative phosphorylation and energy-metabolism pathways. With statins as the use case, biochemical pathway analysis on the local scale implicated a role for CPT2 in statin-induced perturbation of energy homeostasis, which is in agreement with reports of statin-CPT2 interaction. Considering the complexity of human biology, an integrative multiple-approach analysis composed of a biochemical flux network, pharmacological on- and off-target networks, and transcriptomic signature is important for understanding drug safety and for providing insight into clinical gene-drug interactions.

Systems pharmacology modeling: an approach to improving drug safety.

Advances in systems biology in conjunction with the expansion in knowledge of drug effects and diseases present an unprecedented opportunity to extend traditional pharmacokinetic and pharmacodynamic modeling/analysis to conduct systems pharmacology modeling. Many drugs that cause liver injury and myopathies have been studied extensively. Mitochondrion-centric systems pharmacology modeling is important since drug toxicity across a large number of pharmacological classes converges to mitochondrial injury and death. Approaches to systems pharmacology modeling of drug effects need to consider drug exposure, organelle and cellular phenotypes across all key cell types of human organs, organ-specific clinical biomarkers/phenotypes, gene-drug interaction and immune responses. Systems modeling approaches, that leverage the knowledge base constructed from curating a selected list of drugs across a wide range of pharmacological classes, will provide a critically needed blueprint for making informed decisions to reduce the rate of attrition for drugs in development and increase the number of drugs with an acceptable benefit/risk ratio.

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.

Ataxin-3 is a multivalent ligand for the parkin Ubl domain.

The ubiquitin signaling pathway consists of hundreds of enzymes that are tightly regulated for the maintenance of cell homeostasis. Parkin is an E3 ubiquitin ligase responsible for conjugating ubiquitin onto a substrate protein, which itself can be ubiquitinated. Ataxin-3 performs the opposing function as a deubiquitinating enzyme that can remove ubiquitin from parkin. In this work, we have identified the mechanism of interaction between the ubiquitin-like (Ubl) domain from parkin and three C-terminal ubiquitin-interacting motifs (UIMs) in ataxin-3. (1)H-(15)N heteronuclear single-quantum coherence titration experiments revealed that there are weak direct interactions between all three individual UIM regions of ataxin-3 and the Ubl domain. Each UIM utilizes the exposed ß-grasp surface of the Ubl domain centered around the I44 patch that did not vary in the residues involved or the surface size as a function of the number of ataxin-3 UIMs involved. Further, the apparent dissociation constant for ataxin-3 decreased as a function of the number of UIM regions used in experiments. A global multisite fit of the nuclear magnetic resonance titration data, based on three identical binding ligands, resulted in a KD of 669 ± 62 µM for each site. Our observations support a multivalent ligand binding mechanism employed by the parkin Ubl domain to recruit multiple UIM regions in ataxin-3 and provide insight into how these two proteins function together in ubiquitination-deubiquitination pathways.

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.

Quantitative Systems Pharmacology for Rare Disease Drug Development.

Though hundreds of drugs have been approved by the US Food and Drug Administration (FDA) for treating various rare diseases, most rare diseases still lack FDA-approved therapeutics. To identify the opportunities for developing therapies for these diseases, the challenges of demonstrating the efficacy and safety of a drug for treating a rare disease are highlighted herein. Quantitative systems pharmacology (QSP) has increasingly been used to inform drug development; our analysis of QSP submissions received by FDA showed that there were 121 submissions as of 2022, for informing rare disease drug development across development phases and therapeutic areas. Examples of published models for inborn errors of metabolism, non-malignant hematological disorders, and hematological malignancies were briefly reviewed to shed light on use of QSP in drug discovery and development for rare diseases. Advances in biomedical research and computational technologies can potentially enable QSP simulation of the natural history of a rare disease in the context of its clinical presentation and genetic heterogeneity. With this function, QSP may be used to conduct in-silico trials to overcome some of the challenges in rare disease drug development. QSP may play an increasingly important role in facilitating development of safe and effective drugs for treating rare diseases with unmet medical needs.

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.

Autologous T cell therapy for MAGE-A4+ solid cancers in HLA-A*02+ patients: a phase 1 trial.

Affinity-optimized T cell receptors can enhance the potency of adoptive T cell therapy. Afamitresgene autoleucel (afami-cel) is a human leukocyte antigen-restricted autologous T cell therapy targeting melanoma-associated antigen A4 (MAGE-A4), a cancer/testis antigen expressed at varying levels in multiple solid tumors. We conducted a multicenter, dose-escalation, phase 1 trial in patients with relapsed/refractory metastatic solid tumors expressing MAGE-A4, including synovial sarcoma (SS), ovarian cancer and head and neck cancer ( NCT03132922 ). The primary endpoint was safety, and the secondary efficacy endpoints included overall response rate (ORR) and duration of response. All patients (N = 38, nine tumor types) experienced Grade ≥3 hematologic toxicities; 55% of patients (90% Grade ≤2) experienced cytokine release syndrome. ORR (all partial response) was 24% (9/38), 7/16 (44%) for SS and 2/22 (9%) for all other cancers. Median duration of response was 25.6 weeks (95% confidence interval (CI): 12.286, not reached) and 28.1 weeks (95% CI: 12.286, not reached) overall and for SS, respectively. Exploratory analyses showed that afami-cel infiltrates tumors, has an interferon-γ-driven mechanism of action and triggers adaptive immune responses. In addition, afami-cel has an acceptable benefit-risk profile, with early and durable responses, especially in patients with metastatic SS. Although the small trial size limits conclusions that can be drawn, the results warrant further testing in larger studies.

Modeling and simulation of the effect of proton pump inhibitors on magnesium homeostasis. 1. Oral absorption of magnesium.

Prolonged use of proton pump inhibitors has reportedly caused rare clinically symptomatic hypomagnesemia. A review of the literature suggests PPI drugs may impair intestinal magnesium absorption. With the goal of preventing PPI-induced hypomagnesemia, an oral absorption-centric model was developed by referencing literature data. Our modeling with human data reveals that magnesium absorption is substantial in the distal intestine. We then perform simulations by referring to the reported reduction in mid to distal intestinal pH caused by one week of oral esomeprazole, and to reported reduction of the divalent cation-sensitive current when the carboxyl side chains of glutamic and aspartic residues in the binding channels of TRPM6/TRPM7 were neutralized. Our simulations reveal that short-term PPI therapy may cause a very small reduction (5%) in the serum magnesium level, which is qualitatively consistent with the reported 1% reduction in magnesium absorption following 1 week of omeprazole in humans. Simulations provide insight into the benefit of frequent but small dose of magnesium supplementation in maintaining the serum magnesium level when magnesium deficiency occurs.

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.

Phase 1 Clinical Trial Evaluating the Safety and Anti-Tumor Activity of ADP-A2M10 SPEAR T-Cells in Patients With MAGE-A10+ Head and Neck, Melanoma, or Urothelial Tumors.

Background: ADP-A2M10 specific peptide enhanced affinity receptor (SPEAR) T-cells are genetically engineered autologous T-cells that express a high-affinity melanoma-associated antigen (MAGE)-A10-specific T-cell receptor (TCR) targeting MAGE-A10-positive tumors in the context of human leukocyte antigen (HLA)-A*02. ADP-0022-004 is a phase 1, dose-escalation trial to evaluate the safety and anti-tumor activity of ADP-A2M10 in three malignancies (https://clinicaltrials.gov: NCT02989064). Methods: Eligible patients were HLA-A*02 positive with advanced head and neck squamous cell carcinoma (HNSCC), melanoma, or urothelial carcinoma (UC) expressing MAGE-A10. Patients underwent apheresis; T-cells were isolated, transduced with a lentiviral vector containing the MAGE-A10 TCR, and expanded. Patients underwent lymphodepletion with fludarabine and cyclophosphamide prior to receiving ADP-A2M10. ADP-A2M10 was administered in two dose groups receiving 0.1×109 and >1.2 to 6×109 transduced cells, respectively, and an expansion group receiving 1.2 to 15×109 transduced cells. Results: Ten patients (eight male and two female) with HNSCC (four), melanoma (three), and UC (three) were treated. Three patients were treated in each of the two dose groups, and four patients were treated in the expansion group. The most frequently reported adverse events grade ≥3 were leukopenia (10), lymphopenia (10), neutropenia (10), anemia (nine), and thrombocytopenia (five). Two patients reported cytokine release syndrome (one each with grade 1 and grade 3), with resolution. Best response included stable disease in four patients, progressive disease in five patients, and not evaluable in one patient. ADP-A2M10 cells were detectable in peripheral blood from patients in each dose group and the expansion group and in tumor tissues from patients in the higher dose group and the expansion group. Peak persistence was greater in patients from the higher dose group and the expansion group compared with the lower dose group. Conclusions: ADP-A2M10 has shown an acceptable safety profile with no evidence of toxicity related to off-target binding or alloreactivity in these malignancies. Persistence of ADP-A2M10 in the peripheral blood and trafficking of ADP-A2M10 into the tumor was demonstrated. Because MAGE-A10 expression frequently overlaps with MAGE-A4 expression in tumors and responses were observed in the MAGE-A4 trial (NCT03132922), this clinical program closed, and trials with SPEAR T-cells targeting the MAGE-A4 antigen are ongoing.

Phase I clinical trial evaluating the safety and efficacy of ADP-A2M10 SPEAR T cells in patients with MAGE-A10+ advanced non-small cell lung cancer.

BACKGROUND: ADP-A2M10 specific peptide enhanced affinity receptor (SPEAR) T cells (ADP-A2M10) are genetically engineered autologous T cells that express a high-affinity melanoma-associated antigen A10 (MAGE-A10)-specific T-cell receptor (TCR) targeting MAGE-A10+ tumors in the context of human leukocyte antigen (HLA)-A*02. ADP-0022-003 was a phase I dose-escalation trial that aimed to evaluate the safety and antitumor activity of ADP-A2M10 in non-small cell lung cancer (NSCLC) (NCT02592577). METHODS: Eligible patients were HLA-A*02 positive with advanced NSCLC expressing MAGE-A10. Patients underwent apheresis; T cells were isolated, transduced with a lentiviral vector containing the TCR targeting MAGE-A10, and expanded. Patients underwent lymphodepletion with varying doses/schedules of fludarabine and cyclophosphamide prior to receiving ADP-A2M10. ADP-A2M10 were administered at 0.08-0.12×109 (dose group 1), 0.5-1.2×109 (dose group 2), and 1.2-15×109 (dose group 3/expansion) transduced cells. RESULTS: Eleven patients (male, n=6; female, n=5) with NSCLC (adenocarcinoma, n=8; squamous cell carcinoma, n=3) were treated. Five, three, and three patients received cells in dose group 1, dose group 2, and dose group 3/expansion, respectively. The most frequently reported grade ≥3 adverse events were lymphopenia (n=11), leukopenia (n=10), neutropenia (n=8), anemia (n=6), thrombocytopenia (n=5), and hyponatremia (n=5). Three patients presented with cytokine release syndrome (grades 1, 2, and 4, respectively). One patient received the highest dose of lymphodepletion (fludarabine 30 mg/m2 on days -5 to -2 and cyclophosphamide 1800 mg/m2 on days -5 to -4) prior to a second infusion of ADP-A2M10 and had a partial response, subsequently complicated by aplastic anemia and death. Responses included: partial response (after second infusion; one patient), stable disease (four patients), clinical or radiographic progressive disease (five patients), and not evaluable (one patient). ADP-A2M10 were detectable in peripheral blood and in tumor tissue. Peak persistence was higher in patients who received higher doses of ADP-A2M10. CONCLUSIONS: ADP-A2M10 demonstrated an acceptable safety profile and no evidence of toxicity related to off-target binding or alloreactivity. There was persistence of ADP-A2M10 in peripheral blood as well as ADP-A2M10 trafficking into the tumor. Given the discovery that MAGE-A10 and MAGE-A4 expression frequently overlap, this clinical program closed as trials with SPEAR T cells targeting MAGE-A4 are ongoing.

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.