Translating New Science Into the Drug Review Process: The US FDA's Division of Applied Regulatory Science

Antibody dependent cellular cytotoxicity-inducing anti-EGFR antibodies as effective therapeutic option for cutaneous melanoma resistant to BRAF inhibitors

Introduction

About 50% of cutaneous melanoma (CM) patients present activating BRAF mutations that can be effectively targeted by BRAF inhibitors (BRAFi). However, 20% of CM patients exhibit intrinsic drug resistance to BRAFi, while most of the others develop adaptive resistance over time. The mechanisms involved in BRAFi resistance are disparate and globally seem to rewire the cellular signaling profile by up-regulating different receptor tyrosine kinases (RTKs), such as the epidermal growth factor receptor (EGFR). RTKs inhibitors have not clearly demonstrated anti-tumor activity in BRAFi resistant models. To overcome this issue, we wondered whether the shared up-regulated RTK phenotype associated with BRAFi resistance could be exploited by using immune weapons as the antibody-dependent cell cytotoxicity (ADCC)-mediated effect of anti-RTKs antibodies, and kill tumor cells independently from the mechanistic roots.

Methods and results

By using an in vitro model of BRAFi resistance, we detected increased membrane expression of EGFR, both at mRNA and protein level in 4 out of 9 BRAFi-resistant (VR) CM cultures as compared to their parental sensitive cells. Increased EGFR phosphorylation and AKT activation were observed in the VR CM cultures. EGFR signaling appeared dispensable for maintaining resistance, since small molecule-, antibody- and CRISPR-targeting of EGFR did not restore sensitivity of VR cells to BRAFi. Importantly, immune-targeting of EGFR by the anti-EGFR antibody cetuximab efficiently and specifically killed EGFR-expressing VR CM cells, both in vitro and in humanized mouse models in vivo, triggering ADCC by healthy donors' and patients' peripheral blood cells.

Conclusion

Our data demonstrate the efficacy of immune targeting of RTKs expressed by CM relapsing on BRAFi, providing the proof-of-concept supporting the assessment of anti-RTK antibodies in combination therapies in this setting. This strategy might be expected to concomitantly trigger the crosstalk of adaptive immune response leading to a complementing T cell immune rejection of tumors.

"Appraisal of state-of-the-art" The 2021 Distinguished Service Award of the Safety Pharmacology Society: Reflecting on the past to tackle challenges ahead

This appraisal of state-of-the-art manuscript highlights and expands upon the thoughts conveyed in the lecture of Dr. Jean-Pierre Valentin, recipient of the 2021 Distinguished Service Award of the Safety Pharmacology Society, given on the 2nd December 2021. The article reflects on the strengths, weaknesses, opportunities, and threats that surrounded the evolution of safety and secondary pharmacology over the last 3 decades with a particular emphasis on pharmaceutical drug development delivery, scientific and technological innovation, complexities of regulatory framework and people leadership and development. The article further built on learnings from past experiences to tackle constantly emerging issues and evolving landscape whilst being cognizant of the challenges facing these disciplines in the broader drug development and societal context.

Humanized mouse models for immuno-oncology research

Immunotherapy has emerged as a promising treatment paradigm for many malignancies and is transforming the drug development landscape. Although immunotherapeutic agents have demonstrated clinical efficacy, they are associated with variable clinical responses, and substantial gaps remain in our understanding of their mechanisms of action and specific biomarkers of response. Currently, the number of preclinical models that faithfully recapitulate interactions between the human immune system and tumours and enable evaluation of human-specific immunotherapies in vivo is limited. Humanized mice, a term that refers to immunodeficient mice co-engrafted with human tumours and immune components, provide several advantages for immuno-oncology research. In this Review, we discuss the benefits and challenges of the currently available humanized mice, including specific interactions between engrafted human tumours and immune components, the development and survival of human innate immune populations in these mice, and approaches to study mice engrafted with matched patient tumours and immune cells. We highlight the latest advances in the generation of humanized mouse models, with the aim of providing a guide for their application to immuno-oncology studies with potential for clinical translation.

2000-2023 over two decades of ICH S7A: has the time come for a revamp?

The ICH S7A guideline on safety pharmacology studies released over 20 years ago largely achieved its objective "to help protect clinical trial participants and patients receiving marketed products from potential adverse effects of pharmaceuticals". Although, Phase I clinical trials are generally very safe, the incidence and severity of adverse events, the safety related attrition and product withdrawal remain elevated during late-stage clinical development and post approval, a proportion of which can be attributed at least in part to safety pharmacology related issues. Considering the latest scientific and technological advancements in drug safety science, the paradigm shift of the drug discovery and development process and the continuously evolving regulatory landscape, we recommend revisiting, adapting and evolving the ICH S7A guideline. This might offer opportunities i) to select and progress optimized drugs with increased confidence in success, ii) to refine and adapt the clinical monitoring at all stages of clinical development resulting in an optimized benefit/risk assessment, iii) to increase likelihood of regulatory acceptance in a way compatible with an expedited and streamlined drug discovery and development process to benefit patients and iv) to avoid the unnecessary use of animals in 'tick-the-box' studies and encourage alternative approaches. As presented in the article, several options could be envisioned to revisit and adapt the ICH S7A taking into consideration several key features.

New science, drug regulation, and emergent public health issues: The work of FDA's division of applied regulatory science

The U.S. Food and Drug Administration (FDA) Division of Applied Regulatory Science (DARS) moves new science into the drug review process and addresses emergent regulatory and public health questions for the Agency. By forming interdisciplinary teams, DARS conducts mission-critical research to provide answers to scientific questions and solutions to regulatory challenges. Staffed by experts across the translational research spectrum, DARS forms synergies by pulling together scientists and experts from diverse backgrounds to collaborate in tackling some of the most complex challenges facing FDA. This includes (but is not limited to) assessing the systemic absorption of sunscreens, evaluating whether certain drugs can convert to carcinogens in people, studying drug interactions with opioids, optimizing opioid antagonist dosing in community settings, removing barriers to biosimilar and generic drug development, and advancing therapeutic development for rare diseases. FDA tasks DARS with wide ranging issues that encompass regulatory science; DARS, in turn, helps the Agency solve these challenges. The impact of DARS research is felt by patients, the pharmaceutical industry, and fellow regulators. This article reviews applied research projects and initiatives led by DARS and conducts a deeper dive into select examples illustrating the impactful work of the Division.

How resilient were we in 2021? Results of a LinkedIn Survey including biomedical and pharmaceutical professionals using the Benatti Resiliency Model

Enhancing resiliency should elevate innovation and efficiency in biomedical research and development (R&D); however, compared with other professions, data on practice of resilience is lacking. Using the Benatti Resiliency Model (5 anchors: Well-Being, Self-Awareness, Brand, Connection, and Innovation), we surveyed professionals, including those in biomedical and pharmaceutical R&D. A structured LinkedIn questionnaire (March 16-May 23, 2021), surveyed each model anchor using five categories. One hundred fifty-eight participants (~6% student/trainee, 18%, 27%, and 49% in 1-5, 5-15 or >15 years post-terminal degree) took the survey (90 in biomedical and pharmaceutical R&D). Over 50% chose "always"/"often" across questions, except external influence or engagement. The question with one of the lowest "always" scores (~15%) was "I get feedback on my influence and impact in my career" in Brand, highlighting areas for leadership development and coaching. In the anchor of Well-being, nutrition and stress management also received some lowest "always" scores (~15% for both). Connection and Innovation scores trended slightly higher in biomedical and pharmaceutical R&D. No students/trainees chose "always" in Brand, indicating evolution of brand maturity over time. Self- and survey-assessed resiliency scores were associated (r_s  = 0.37, p < 0.0001). Our survey yielded actionable insights on Resilience, including "best practices" through an open-ended question for one thing most useful to boost resilience in the survey and is the first application of the Benatti Model for crowdsourced research.

W. Emerging technologies and their impact on regulatory science

There is an evolution and increasing need for the utilization of emerging cellular, molecular and in silico technologies and novel approaches for safety assessment of food, drugs, and personal care products. Convergence of these emerging technologies is also enabling rapid advances and approaches that may impact regulatory decisions and approvals. Although the development of emerging technologies may allow rapid advances in regulatory decision making, there is concern that these new technologies have not been thoroughly evaluated to determine if they are ready for regulatory application, singularly or in combinations. The magnitude of these combined technical advances may outpace the ability to assess fit for purpose and to allow routine application of these new methods for regulatory purposes. There is a need to develop strategies to evaluate the new technologies to determine which ones are ready for regulatory use. The opportunity to apply these potentially faster, more accurate, and cost-effective approaches remains an important goal to facilitate their incorporation into regulatory use. However, without a clear strategy to evaluate emerging technologies rapidly and appropriately, the value of these efforts may go unrecognized or may take longer. It is important for the regulatory science field to keep up with the research in these technically advanced areas and to understand the science behind these new approaches. The regulatory field must understand the critical quality attributes of these novel approaches and learn from each other's experience so that workforces can be trained to prepare for emerging global regulatory challenges. Moreover, it is essential that the regulatory community must work with the technology developers to harness collective capabilities towards developing a strategy for evaluation of these new and novel assessment tools.

3D cell culture models: Drug pharmacokinetics, safety assessment, and regulatory consideration

Nonclinical testing has served as a foundation for evaluating potential risks and effectiveness of investigational new drugs in humans. However, the current two-dimensional (2D) in vitro cell culture systems cannot accurately depict and simulate the rich environment and complex processes observed in vivo, whereas animal studies present significant drawbacks with inherited species-specific differences and low throughput for increased demands. To improve the nonclinical prediction of drug safety and efficacy, researchers continue to develop novel models to evaluate and promote the use of improved cell- and organ-based assays for more accurate representation of human susceptibility to drug response. Among others, the three-dimensional (3D) cell culture models present physiologically relevant cellular microenvironment and offer great promise for assessing drug disposition and pharmacokinetics (PKs) that influence drug safety and efficacy from an early stage of drug development. Currently, there are numerous different types of 3D culture systems, from simple spheroids to more complicated organoids and organs-on-chips, and from single-cell type static 3D models to cell co-culture 3D models equipped with microfluidic flow control as well as hybrid 3D systems that combine 2D culture with biomedical microelectromechanical systems. This article reviews the current application and challenges of 3D culture systems in drug PKs, safety, and efficacy assessment, and provides a focused discussion and regulatory perspectives on the liver-, intestine-, kidney-, and neuron-based 3D cellular models.

Assessing the impact of expert knowledge on ICH M7 (Q)SAR predictions. Is expert review still needed?

The ICH M7 (R1) guideline recommends the use of complementary (Q)SAR models to assess the mutagenic potential of drug impurities as a state-of-the-art, high-throughput alternative to empirical testing. Additionally, it includes a provision for the application of expert knowledge to increase prediction confidence and resolve conflicting calls. Expert knowledge, which considers structural analogs and mechanisms of activity, has been valuable when models return an indeterminate (equivocal) result or no prediction (out-of-domain). A retrospective analysis of 1002 impurities evaluated in drug regulatory applications between April 2017 and March 2019 assessed the impact of expert review on (Q)SAR predictions. Expert knowledge overturned the default predictions for 26% of the impurities and resolved 91% of equivocal predictions and 75% of out-of-domain calls. Of the 261 overturned default predictions, 15% were upgraded to equivocal or positive and 79% were downgraded to equivocal or negative. Chemical classes with the most overturns were primary aromatic amines (46%), aldehydes (45%), Michael-reactive acceptors (37%), and non-primary alkyl halides (33%). Additionally, low confidence predictions were the most often overturned. Collectively, the results suggest that expert knowledge continues to play an important role in an ICH M7 (Q)SAR prediction workflow and triaging predictions based on chemical class and probability can improve (Q)SAR review efficiency.

Network model-based screen for FDA-approved drugs affecting cardiac fibrosis

Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.

Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses

The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.

Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses

The International Council for Harmonization (ICH) M7 guideline describes a hazard assessment process for impurities that have the potential to be present in a drug substance or drug product. In the absence of adequate experimental bacterial mutagenicity data, (Q)SAR analysis may be used as a test to predict impurities' DNA reactive (mutagenic) potential. However, in certain situations, (Q)SAR software is unable to generate a positive or negative prediction either because of conflicting information or because the impurity is outside the applicability domain of the model. Such results present challenges in generating an overall mutagenicity prediction and highlight the importance of performing a thorough expert review. The following paper reviews pharmaceutical and regulatory experiences handling such situations. The paper also presents an analysis of proprietary data to help understand the likelihood of misclassifying a mutagenic impurity as non-mutagenic based on different combinations of (Q)SAR results. This information may be taken into consideration when supporting the (Q)SAR results with an expert review, especially when out-of-domain results are generated during a (Q)SAR evaluation.

Construction and application of (Q)SAR models to predict chemical-induced in vitro chromosome aberrations

In drug development, genetic toxicology studies are conducted using in vitro and in vivo assays to identify potential mutagenic and clastogenic effects, as outlined in the International Council for Harmonisation (ICH) S2 regulatory guideline. (Quantitative) structure-activity relationship ((Q)SAR) models that predict assay outcomes can be used as an early screen to prioritize pharmaceutical candidates, or later during product development to evaluate safety when experimental data are unavailable or inconclusive. In the current study, two commercial QSAR platforms were used to build models for in vitro chromosomal aberrations in Chinese hamster lung (CHL) and Chinese hamster ovary (CHO) cells. Cross-validated CHL model predictive performance showed sensitivity of 80 and 82%, and negative predictivity of 75 and 76% based on 875 training set compounds. For CHO, sensitivity of 61 and 67% and negative predictivity of 68 and 74% was achieved based on 817 training set compounds. The predictive performance of structural alerts in a commercial expert rule-based SAR software was also investigated and showed positive predictivity of 48-100% for selected alerts. Case studies examining incorrectly-predicted compounds, non-DNA-reactive clastogens, and recently-approved pharmaceuticals are presented, exploring how an investigational approach using similarity searching and expert knowledge can improve upon individual (Q)SAR predictions of the clastogenicity of drugs.

Regulatory Science - An Underappreciated Component of Translational Research

Translational science refers to translating basic scientific findings to practical application (i.e., 'bench-to-bedside'). An underappreciated aspect of translational science is regulatory science. Herein, we focus on the importance of regulatory science to facilitate development of innovative new drugs and optimize use of approved drugs, with a call for community participation.