Drug Development Decisions Research Articles

Early Prediction and Impact Assessment of CYP3A4-Related Drug-Drug Interactions for Small-Molecule Anticancer Drugs Using Human-CYP3A4-Transgenic Mouse Models.

Early detection of drug-drug interactions (DDIs) can facilitate timely drug development decisions, prevent unnecessary restrictions on patient enrollment, resulting in clinical study populations that are not representative of the indicated study population, and allow for appropriate dose adjustments to ensure safety in clinical trials. All of these factors contribute to a streamlined drug approval process and enhanced patient safety. Here we describe a new approach for early prediction of the magnitude of change in exposure for cytochrome P450 (P450) CYP3A4-related DDIs of small-molecule anticancer drugs based on the model-based extrapolation of human-CYP3A4-transgenic mice pharmacokinetics to humans. Victim drugs brigatinib and lorlatinib were evaluated with the new approach in combination with the perpetrator drugs itraconazole and rifampicin. Predictions of the magnitude of change in exposure deviated at most 0.99- to 1.31-fold from clinical trial results for inhibition with itraconazole, whereas exposure predictions for the induction with rifampicin were less accurate, with deviations of 0.22- to 0.48-fold. Results for the early prediction of DDIs and their clinical impact appear promising for CYP3A4 inhibition, but validation with more victim and perpetrator drugs is essential to evaluate the performance of the new method. SIGNIFICANCE STATEMENT: The described method offers an alternative for the early detection and assessment of potential clinical impact of CYP3A4-related drug-drug interactions. The model was able to adequately describe the inhibition of CYP3A4 metabolism and the subsequent magnitude of change in exposure. However, it was unable to accurately predict the magnitude of change in exposure of victim drugs in combination with an inducer.

Framework for Multistakeholder Patient Registries in the Field of Rare Diseases: Focus on Neurogenetic Diseases.

Progress in genetic diagnosis and orphan drug legislation has opened doors to new therapies in rare neurogenetic diseases (RNDs). Innovative therapies such as gene therapy can improve patients' quality of life but come with academic, regulatory, and financial challenges. Registries can play a pivotal role in generating evidence to tackle these, but their development requires multidisciplinary knowledge and expertise. This study aims to develop a practical framework for creating and implementing patient registries addressing common challenges and maximizing their impact on care, research, drug development, and regulatory decision making with a focus on RNDs. A comprehensive 3-step literature and qualitative research approach was used to develop the framework. A qualitative systematic literature review was conducted, extracting guidance and practices leading to the draft framework. Subsequently, we interviewed representatives of 5 established international RND registries to add learnings from hands-on experiences to the framework. Expert input on the draft framework was sought in digital multistakeholder focus groups to refine the framework. The literature search; interviews with 5 registries; and focus groups with patient representatives (n = 4), clinicians (n = 6), regulators, health technology assessment (HTA) bodies and payers (n = 7), industry representatives (n = 7), and data/information technology (IT) specialists (n = 5) informed development of the framework. It covers the interests of different stakeholders, purposes for data utilization, data aspects, IT infrastructure, governance, and financing of rare disease registries. Key principles include that data should be rapidly accessible, independent, and trustworthy. Governance should involve multiple stakeholders. In addition, data should be highly descriptive, machine-readable, and accessible through a shared infrastructure and not spread over multiple isolated repositories. Sustainable and independent financing of registries is deemed important but remains challenging because of a lack of widely supported funding models. The proposed framework will guide stakeholders in establishing or improving rare disease registries that fulfill requirements of academics and patients as well as regulators, HTA bodies, and commercial parties. There is a need for more clarity regarding quality requirements for registries in regulatory and HTA context. In addition, independent financing models for registries should be developed, as well as well-defined policies on technical uniformity in health data.

Artificial intelligence as a tool in drug discovery and development.

The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

Utility of Biomarker-Informed Drug Interaction Evaluation in Drug Development and Regulatory Decision Making.

The measurement of endogenous biomarkers in plasma and urine before and after administration of an investigational drug in a clinical study may provide an early indication of its drug-drug interaction (DDI) potential via a specific pathway. In the first international harmonized guideline on drug interaction studies, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M12, endogenous biomarkers have been recognized as an emerging approach in the transporter- and enzyme-based DDI risk assessment. Clinical Pharmacology Roundtable Conference 2024 held at Pharmaceuticals and Medical Devices Agency (PMDA) brought together experts from regulatory agencies, academia, and industries to discuss potential advantages and challenges of the biomarkers approach in drug development and regulatory decision making. This meeting report facilitates stakeholders involved in drug development in better understanding the utility of biomarker approaches and promotes early implementation of biomarker-informed DDI evaluation in regulatory use.

A Modern Curriculum for Training Scientists in Model-Informed Drug Development: Progress Report on FDA Grant to Train Regulatory Scientists.

Under US Food and Drug Administration (FDA) grant (2U18FD005320-06), the Critical Path Institute (C-Path) and experienced private sector partners collaborated with global health organizations to create didactic video materials in an e-learning format on model-informed drug development (MIDD) topics relevant to a non-modeling audience. Several multinational pharmaceutical companies contributed case studies illustrating the application of the MIDD approach in practice. Training videos were created and divided into several modules: introducing the MIDD landscape for drug development and regulatory science, a review of various model types used for MIDD, discussions of how models inform drug development and regulatory decisions, future goals of MIDD, and discussions on the interconnectedness of models used for MIDD. Examples and vignettes from stakeholders and thought leaders were included. These educational materials fill a gap between academic and "on the job training" for regulators, academic, and industry scientists, delivering insights and value for those performing modeling and non-modelers reviewing the output of modeling and simulation work. A total of 13 hours of video content is currently available. A small panel of FDA reviewers is currently beta-testing the learning management system (LMS). Future efforts for this MIDD training initiative will include expansion of the content via an expanded and diverse faculty, 1:1 online mentorship sessions, and eventually broader access to this resource consistent with an open science approach and curriculum. The MIDD training LMS can accommodate a diverse learning ecosystem; further development may also accommodate different audiences in the future.

Induction of steatosis in primary human hepatocytes recapitulates key pathophysiological aspects of metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease. Owing to limited available treatment options, novel pre-clinical models for target selection and drug validation are warranted. We have established and extensively characterized a primary human steatotic hepatocyte invitro model system that could guide the development of treatment strategies for MASLD. Cryopreserved primary human hepatocytes from five donors varying in sex and ethnicity were cultured with free fatty acids in a 3D collagen sandwich for 7 days and the development of MASLD was followed by assessing classical hepatocellular functions. As proof of concept, the effects of the drug firsocostat (GS-0976) on invitro MASLD phenotypes were evaluated. Incubation with free fatty acids induced steatosis, insulin resistance, mitochondrial dysfunction, inflammation, and alterations in prominent human gene signatures similar to patients with MASLD, indicating the recapitulation of human MASLD in this system. The application of firsocostat rescued clinically observed fatty liver disease pathologies, highlighting the ability of the invitro system to test the efficacy and potentially characterize the mode of action of drug candidates. Altogether, our human MASLD invitro model system could guide the development and validation of novel targets and drugs for the treatment of MASLD. Due to low drug efficacy and high toxicity, clinical treatment options for metabolic dysfunction-associated steatotic liver disease (MASLD) are currently limited. To facilitate earlier stop-go decisions in drug development, we have established a primary human steatotic hepatocyte invitro model. As the model recapitulates clinically relevant MASLD characteristics at high phenotypic resolution, it can serve as a pre-screening platform and guide target identification and validation in MASLD therapy.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice.

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review,we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

Application and Quality of Model-Based Meta-Analysis in Pharmaceutical Research: A Systematic Cross-Sectional Analysis and Practical Considerations.

Model-based meta-analysis (MBMA) can be used in assisting drug development and optimizing treatment in clinical practice, potentially reducing costs and accelerating drug approval. We aimed to assess the application and quality of MBMA studies. We searched multiple databases to identify MBMA in pharmaceutical research. Eligible MBMA should incorporate pharmacological concepts to construct mathematical models and quantitatively examine and/or predict drug effects. Relevant information was summarized to provide an overview of the application of MBMA. We used AMSTAR-2 and PRISMA 2020 checklists to evaluate the methodological and reporting quality of included MBMA, respectively. A total of 143 MBMA studies were identified. MBMA was increasingly used over time for one or more areas: drug discovery and translational research (n = 8, 5.6%), drug development decision making (n = 42, 29.4%), optimization of clinical trial design (n = 46, 32.2%), medication in special populations (n = 15, 10.5%), and rationality and safety of drug use (n = 71, 49.7%). The included MBMA covered 17 disease areas, with the top three being nervous system diseases (n = 19, 13.2%), endocrine/nutritional/metabolic diseases (n = 17, 11.8%), and neoplasms (n = 16, 11.1%). Of these MBMA studies, 138 (96.5%) were rated as very low quality. The average rate of compliance with PRISMA was only 51.4%. Our findings suggested that MBMA was mainly used to evaluate the efficacy and safety of drugs, with a focus on chronic diseases. The methodological and reporting quality of MBMA should be further improved. Given AMSTAR-2 and PRISMA checklists were not specifically designed for MBMA, adapted assessment checklists for MBMA should be warranted.

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction.

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

Considerations for Industry-Preparing for the FDA Model-Informed Drug Development (MIDD) Paired Meeting Program.

A recent industry perspective published in this journal describes the benefits received by drug companies from participation in the MIDD Pilot Program. Along with the primary objectives of supporting good decision-making in drug development, there were substantial savings in time and development costs. Furthermore, many sponsors reported qualitative benefits such as new learnings and clarity on MIDD strategies and methodology that could be applied to other development programs. Based on the success of the Pilot Program, the FDA recently announced the continuation of the MIDD Paired Meeting Program as part of the Prescription Drug User Fee Act (PDUFA VII). In this report, we describe the collective experiences of industry participants in the MIDD Program to date, including all aspects of the process from meeting request submission to follow-up actions. The purpose is to provide future participants with information to optimize the value of the MIDD Program.

Abstract 7625: The liquid biomarker thymidine kinase activity reflecting proliferation rate can provide guidance in drug development and address Project Optimus

Abstract Thymidine Kinase (TK) plays a basic role in DNA synthesis and cell proliferation. The enzyme is highly cell cycle dependent and correlates with cell growth state. Numerous studies have consistently shown elevated TK activity (TKa) in the blood of patients with solid tumors and active disease. DiviTum® TKa, a clinically and analytically validated FDA cleared assay, measures and quantifies TKa in serum or plasma in humans and animals. TKa as a biomarker offers valuable information at all stages of drug development, from drug efficacy and dose response studies in cell culture and xenograft models, to patient selection and treatment monitoring in clinical trials. The launch of FDA Project Optimus highlights the need for new biomarkers to assist in drug development to identify optimal dose. Using TKa to measure the impact on cell proliferation as a drug evaluation endpoint can confirm mechanism of action efficacy and a minimally effective dose of a new compound rather than a maximally tolerated dose. TKa can be used as a pharmacodynamic tool to improve study endpoints. TKa data in cultured cells and mouse xenografts demonstrates that a complete shutdown of tumor cell proliferation can be observed well before and at much lower drug-doses than when cell death or toxicity is used as the dose determination endpoint. Increased dosing of CDK4/6 inhibitors demonstrates immediate impact on TKa levels in cell lines and provides signals of drug effect at doses where cell viability is unaffected. After initiation of samuraciclib treatment, a CDK7 inhibitor, a significant reduction in TKa levels in blood from metastatic breast cancer (MBC) patients was observed, indicating inhibition of cell cycle progression. For patients on samuraciclib therapy and with stable disease before progression, TKa levels were significantly lower than pre-treatment (p=0.0038). Post-progression TKa levels increased to significantly higher levels than during therapy including the last sample measured before progression (p=0.014). Baseline blood TKa levels are predictive for progression free survival (PFS) allowing for the early stratification of patients with long vs short PFS. In patients with MBC, TKa levels during the first treatment cycle are strongly predictive for PFS. Suppressed TKa levels after 2 and 4 weeks of CDK4/6 inhibitor therapy predict longer PFS than high TKa levels at the same time points (HR 5.65; p< 0.0001). MBC patients monitored >3 months and showing low TKa levels have very low likelihood of progression within 30- to 60 days, <2% and <6%, respectively. TKa is a translational liquid biomarker that bridges results between preclinical and clinical studies, providing fundamental information for drug development decision making. Citation Format: Mattias Bergqvist, Hanna Ritzén, Amy Williams. The liquid biomarker thymidine kinase activity reflecting proliferation rate can provide guidance in drug development and address Project Optimus [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7625.

Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision Making.

While pharmaceutical companies aim to leverage real-world data (RWD) to bridge the gap between clinical drug development and real-world patient outcomes, extant research has mainly focused on the use of social media in a post-approval safety-surveillance setting. Recent regulatory and technological developments indicate that social media may serve as a rich source to expand the evidence base to pre-approval and drug development activities. However, use cases related to drug development have been largely omitted, thereby missing some of the benefits of RWD. In addition, an applied end-to-end understanding of RWD rooted in both industry and regulations is lacking. We aimed to investigate how social media can be used as a source of RWD to support regulatory decision making and drug development in the pharmaceutical industry. We aimed to specifically explore the data pipeline and examine how social-media derived RWD can align with regulatory guidance from the US Food and Drug Administration and industry needs. A machine learning pipeline was developed to extract patient insights related to anticoagulants from X (Twitter) data. These findings were then analysed from an industry perspective, and complemented by interviews with professionals from a pharmaceutical company. The analysis reveals several use cases where RWD derived from social media can be beneficial, particularly in generating hypotheses around patient and therapeutic area needs. We also note certain limitations of social media data, particularly around inferring causality. Social media display considerable potential as a source of RWD for guiding efforts in pharmaceutical drug development and pre-approval settings. Although further regulatory guidance on the use of social media for RWD is needed to encourage its use, regulatory and technological developments are suggested to warrant at least exploratory uses for drug development.

An industry perspective on current QSP trends in drug development.

2023 marks the 10th anniversary of Natpara's submission to the US FDA, which led to the first recorded regulatory interaction where a decision was supported by Quantitative and Systems Pharmacology (QSP) simulations. It had taken about 5 years for the timid QSP discipline to emerge as an effective Model-Informed Drug Development (MIDD) tool with visible impact in the pharmaceutical industry. Since then, the presence of QSP in the regulatory environment has continued to increase, to the point that the Agency reported 60 QSP submissions in 2020 alone, representing ~ 4% of their annual IND submissions [1]. What sort of industry mindset has enabled QSP to reach this level of success? How does QSP fit within the MIDD paradigm? Does QSP mean the same to Discovery and to Clinical Development projects? How do 'platforms' compare to 'fit-for-purpose' QSP models in an industrial setting? Can QSP and empirical Pharmacokinetic-Pharmacodynamic (PKPD) modelling be complementary? What level of validation is required to inform drug development decisions? This article reflects on all these questions, in particular addressing those audiences with limited line-of-sight into the drug industry decision-making machinery.

Application of Model-Informed Drug Development in Dose Selection and Optimization for siRNA Therapies.

The application of model-informed drug development (MIDD) has revolutionized drug development and regulatory decision making, transforming the process into one that is more efficient, effective, and patient centered. A critical application of MIDD is to facilitate dose selection and optimization, which play a pivotal role in improving efficacy, safety, and tolerability profiles of a candidate drug. With the surge of interest in small interfering RNA (siRNA) drugs as a promising class of therapeutics, their applications in various disease areas have been extensively studied preclinically. However, dosing selection and optimization experience for siRNA in humans is limited. Unique challenges exist for the dose evaluation of siRNA due to the temporal discordance between pharmacokinetic and pharmacodynamic profiles, as well as limited available clinical experience and considerable interindividual variability. This review highlights the pivotal role of MIDD in facilitating dose selection and optimization for siRNA therapeutics. Based on past experiences with approved siRNA products, MIDD has demonstrated its ability to aid in dose selection for clinical trials and enabling optimal dosing for the general patient population. In addition, MIDD presents an opportunity for dose individualization based on patient characteristics, enhancing the precision and effectiveness of siRNA therapeutics. In conclusion, the integration of MIDD offers substantial advantages in navigating the complex challenges of dose selection and optimization in siRNA drug development, which in turn accelerates the development process, supports regulatory decision making, and ultimately improves the clinical outcomes of siRNA-based therapies, fostering advancements in precision medicine across a diverse range of diseases.

OP40 Transcriptomic signature of response to vedolizumab in patients with moderate-to-severe ulcerative colitis: Results from an international, multicentre, retrospective cohort study

Abstract Background Transcriptomic analysis has the potential to facilitate drug development and clinical decision making in inflammatory bowel disease. Current understanding of pharmacodynamic response to vedolizumab is limited by a lack of publicly available mucosal biopsy RNA samples/sequencing data and corresponding clinical information. We aimed to identify gene signatures associated with response to vedolizumab in ulcerative colitis (UC) patients by pooling data from 3 international sites. Methods Patients receiving vedolizumab for the treatment of moderate-to-severe UC at Hospital Clínic de Barcelona (IDIBAPS), Mount Sinai Hospital (MSH), and University of California, San Diego (UCSD) with baseline and post-treatment (week 14 [± 4 weeks]) mucosal biopsy RNA-sequencing data and Mayo Clinic endoscopic subscores (MCES) were eligible for inclusion. Endoscopic response was defined as an MCES <1. Demographic and clinical data were collected. Differential expressed genes (DEGs) between baseline and week 14 were identified using linear mixed models with a >1.5 or <-1.5-fold change (adjusted P-value <.05). Enrichment was performed using Reactome pathway analysis and a previously published UC gene signature (adjusted P-value <.05).1 Analyses were performed using R version 4.3.0. Results Twenty-three patients were included (IDIBAPS: n=12; MSH: n=8; UCSD: n=3). Site-specific differences in sex, surgical history, and corticosteroid use were observed (Table). Analysing data by site yielded a relatively small number of DEGs between baseline and week 14 (IDIBAPS=57 [27 up-regulated, 30 down-regulated]; MSH=0; UCSD=0) among endoscopic responders (IDIBAPS: n=5; MSH: n=7; UCSD: n=2). Combining data from the 3 sites increased the number of DEGs among endoscopic responders at week 14 (n=14) to 1162 (662 up-regulated and 500 down-regulated). The down-regulated DEGs were enriched for genes involved in 37 of 103 up-regulated Reactome pathways in UC, including neutrophil degranulation, interleukin-4 and interleukin-13 signalling, interleukin-10 signalling, and integrin cell surface interactions. The up-regulated DEGs were enriched for genes involved in 1 of 39 down-regulated Reactome pathways in UC (Drug ADME). Conclusion We defined a pharmacodynamic signature using mucosal biopsies from UC patients who had endoscopic response to vedolizumab at week 14. The signature was enriched for several, but not all, pathways involved in UC. In addition to providing insight into vedolizumab’s mechanism of action, we underscored the utility of multi-site collaboration to access data, increase statistical power, and enhance the generalizability of research findings. Reference: 1. Linggi et al. Sci Rep 2021;11:18243.

The integrated stress response-related expression of CHOP due to mitochondrial toxicity is a warning sign for DILI liability.

Drug-induced liver injury (DILI) is one of the most frequent reasons for failure of drugs in clinical trials or market withdrawal. Early assessment of DILI risk remains a major challenge during drug development. Here, we present a mechanism-based weight-of-evidence approach able to identify certain candidate compounds with DILI liabilities due to mitochondrial toxicity. A total of 1587 FDA-approved drugs and 378 kinase inhibitors were screened for cellular stress response activation associated with DILI using an imaging-based HepG2 BAC-GFP reporter platform including the integrated stress response (CHOP), DNA damage response (P21) and oxidative stress response (SRXN1). In total 389, 219 and 104 drugs were able to induce CHOP-GFP, P21-GFP and SRXN1-GFP expression at 50 μM respectively. Concentration response analysis identified 154 FDA-approved drugs as critical CHOP-GFP inducers. Based on predicted and observed (pre-)clinical DILI liabilities of these drugs, nine antimycotic drugs (e.g. butoconazole, miconazole, tioconazole) and 13 central nervous system (CNS) agents (e.g. duloxetine, fluoxetine) were selected for transcriptomic evaluation using whole-genome RNA-sequencing of primary human hepatocytes. Gene network analysis uncovered mitochondrial processes, NRF2 signalling and xenobiotic metabolism as most affected by the antimycotic drugs and CNS agents. Both the selected antimycotics and CNS agents caused impairment of mitochondrial oxygen consumption in both HepG2 and primary human hepatocytes. Together, the results suggest that early pre-clinical screening for CHOP expression could indicate liability of mitochondrial toxicity in the context of DILI, and, therefore, could serve as an important warning signal to consider during decision-making in drug development.

Towards a standardized pipeline for tau PET analysis under a GAAIN/LONI and CPAD collaborative framework

AbstractBackgroundImaging tau burden in vivo using Positron Emission Tomography (PET) has tremendous potential for advancing our biological understanding of Alzheimer’s disease. Towards this direction, there is a need for post‐acquisition imaging data ‘harmonization’ tools and analysis methods that reduce variance across studies, sites, and scanners, while retaining the power to study complex disease related effects. The Global Alzheimer’s Association Interactive Network (GAAIN), the Laboratory of Neuro Imaging (LONI), and the Critical Path for Alzheimer’s Disease (CPAD) Consortium have undertaken a collaborative effort to provide a harmonized approach to the analysis of tau PET images.MethodThe GAAIN/LONI Pipeline is a software suite that allows the design and execution of neuroimaging workflows through flexible integration of different image preprocessing software and parallel processing via a computer grid. Each workflow has a distinct fingerprint that can be executed locally on different platforms or remotely via GAAIN, thus increasing interoperability and allowing the use of “harmonized” workflows across different datasets. Tau PET workflows were designed and deployed on the GAAIN/LONI Pipeline based on existing literature. In turn, these tau PET workflows were executed on CPAD tau PET to derive tau PET burden. Clinical validation was performed by quantifying tau PET burden on different clinical groups. Analytical validation was performed by correlating the LONI Pipeline results with independently developed pipelines.ResultWe first show feasibility results as the tau PET workflows are deployed and executed on CPAD data. For clinical validation, we tested whether derived metrics can distinguish between clinical groups. For analytical validation we tested correlations of results with existing methods that have been independently developed. These results provide a unified analysis model that can be utilized from GAAIN/LONI and CPAD to conduct harmonized analyses on multiple datasets. The full capabilities of this model will be explored in subsequent work.ConclusionThe proposed pipeline, which is going to be part of the publicly available GAAIN/LONI platform, should provide researchers with an optimized roadmap that describes how tau PET imaging data should be processed and analyzed for reliability and reproducibility in the context of drug development and regulatory decision making.

Multiple Myeloma Disease Model to Predict PFS Outcomes across Lines of Therapy, an Analysis Based on Commpass Observational Study

Introduction Multiple Myeloma (MM) remains incurable despite advances in treatment. Model-based approaches based on tumor dynamics have successfully been used to optimize drug-development in oncology. Model-derived tumor dynamics also called tumor growth inhibition (TGI) metrics capture the treatment effect as tumor biomarkers and are predictive of the overall survival (OS) benefit, based on TGI-OS models, this mathematical framework has been shown to be drug-independent in most cases. M-protein is measured longitudinally during clinical trials, it reflects tumor burden and is one of the criteria used to assess clinical response according to the International Myeloma Working Group Uniform Response Criteria. A drug-independent link between M-protein dynamic metrics and OS was previously established and confirmed for refractory MM patients. The primary endpoint in MM clinical trials is usually progression free survival (PFS). New immunotherapies are currently in development and there is a need for early and robust drug development decision making. Therefore there is strong interest to expand this modeling framework to leverage M-protein dynamics to predict PFS outcomes. Methods CoMMpass (https://www.themmrf.org/finding-a-cure/personalized-treatment-approaches/) is a prospective, longitudinal, observational study in newly diagnosed symptomatic patients with multiple myeloma. It represents a unique source of clinical data to investigate the link between M-protein dynamic and survival outcomes across lines of therapy and treatments. Individual M-protein and PFS Data from 804 patients within the CoMMpass study were used. Given patients are enrolled at their initial treatment regimen and observed over 8 years, they can receive several lines of therapy. Due to the characteristics of the modeling framework, allowing to analyze only one line of therapy for each patient, the latest line of therapy for each subject was selected for which they had at least 2 M-protein assessments available. M-protein dynamic metrics (M-protein ratio to baseline at different time points, time to growth, growth rate, shrinkage rate) derived using an empirical bi-exponential model, 42 prognostic factors and potential treatment effects were first tested in a univariate analysis using a Cox proportional hazards regression model. Statistically significant covariates in the univariate analysis (p<0.05) were included in a full multivariate parametric distribution survival model. A backward deletion stepwise procedure was performed (p<0.01) to retain covariates in the final PFS model. Model qualification was performed using posterior predictive checks based on Kaplan Meier plots. Results Among 804 patients, 490 patients received only 1 line of therapy (LoT), 157 received 2 LoTs, 93 received 3 LoTs, 31 received 4 LoTs,18 received 5 LoTs, 10 received 6 LoTs, finally 7 patients got up to 7 to 11 LoTs. PFS distribution followed a log-normal distribution. Table 1 shows the parameter estimates of the PFS model. Among all tested M-protein dynamic metrics and prognostic factors, the logarithmic value of the growth rate (log(KG)), in red in Table 1, was found to be the best predictor of PFS. Patients with slower growth rate, who received a transplantation, with higher albumin and lower lactate dehydrogenase values had longer PFS. Those findings are consistent with previous modeling works conducted in MM. Also, the PFS model was confirmed to be drug-independent. As shown in Figure 1, prediction intervals derived from model-based simulations well captured observed PFS data for each line of treatment although the line of therapy is not included as a predictor in the PFS model. Similar results were obtained across treatments. In a next step, external validations assessing the model performance to reproduce Phase 3 clinical trial outcomes will be performed. Conclusions A model linking M-protein dynamic to PFS in MM was developed across a wide range of lines of therapy and treatments. The model can support drug development in MM: enabling early predictions of clinical trial outcomes and inform study design e.g. by simulating the probability of success of upcoming pivotal clinical trials.

Artificial Intelligence: From Buzzword to Useful Tool in Clinical Pharmacology.

The advent of artificial intelligence (AI) in clinical pharmacology and drug development is akin to the dawning of a new era. Previously dismissed as merely technological hype, these approaches have emerged as promising tools in different domains, including health care, demonstrating their potential to empower clinical pharmacology decision making, revolutionize the drug development landscape, and advance patient care. Although challenges remain, the remarkable progress already made signals that the leap from hype to reality is well underway, and AI promises to offer clinical pharmacology new tools and possibilities for optimizing patient care is gradually coming to fruition. This review dives into the burgeoning world of AI and machine learning (ML), showcasing different applications of AI in clinical pharmacology and the impact of successful AI/ML implementation on drug development and/or regulatory decisions. This review also highlights recommendations for areas of opportunity in clinical pharmacology, including data analysis (e.g., handling large data sets, screening to identify important covariates, and optimizing patient population) and efficiencies (e.g., automation, translation, literature curation, and training). Realizing the benefits of AI in drug development and understanding its value will lead to the successful integration of AI tools in our clinical pharmacology and pharmacometrics armamentarium.

Clinically relevant atovaquone-resistant human malaria parasites fail to transmit by mosquito

Long-acting injectable medications, such as atovaquone, offer the prospect of a “chemical vaccine” for malaria, combining drug efficacy with vaccine durability. However, selection and transmission of drug-resistant parasites is of concern. Laboratory studies have indicated that atovaquone resistance disadvantages parasites in mosquitoes, but lack of data on clinically relevant Plasmodium falciparum has hampered integration of these variable findings into drug development decisions. Here we generate atovaquone-resistant parasites that differ from wild type parent by only a Y268S mutation in cytochrome b, a modification associated with atovaquone treatment failure in humans. Relative to wild type, Y268S parasites evidence multiple defects, most marked in their development in mosquitoes, whether from Southeast Asia (Anopheles stephensi) or Africa (An. gambiae). Growth of asexual Y268S P. falciparum in human red cells is impaired, but parasite loss in the mosquito is progressive, from reduced gametocyte exflagellation, to smaller number and size of oocysts, and finally to absence of sporozoites. The Y268S mutant fails to transmit from mosquitoes to mice engrafted with human liver cells and erythrocytes. The severe-to-lethal fitness cost of clinically relevant atovaquone resistance to P. falciparum in the mosquito substantially lessens the likelihood of its transmission in the field.