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.

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.

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.

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 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.

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.

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.