Later Stages Of Drug Development Research Articles

A deep learning based multi-model approach for predicting drug-like chemical compound’s toxicity

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Computational Screening for mAb Colloidal Stability with Coarse-Grained, Molecular-Scale Simulations.

Monoclonal antibodies (mAbs) are an important modality of protein therapeutics with broad applications for numerous diseases. However, colloidal instabilities occurring at high protein concentrations can limit the ability to develop stable, high-concentration liquid dosage forms that are required for patient-centric, device-mediated products. Therefore, it is advantageous to identify colloidally stable mAbs early in the discovery process to ensure that they are selected for development. Experimental screening for colloidal stability can be time- and resource-consuming and is most feasible at the later stages of drug development due to material requirements. Alternatively, computational approaches have emerging potential to provide efficient screening and focus developmental efforts on mAbs with the greatest developability potential, while providing mechanistic relationships for colloidal instability. In this work, coarse-grained, molecular-scale models were fine-tuned to screen for colloidal stability at amino-acid resolution. This model parameterization provides a framework to screen for mAb self-interactions and extrapolate to bulk solution behavior. This approach was applied to a wide array of mAbs under multiple buffer conditions, demonstrating the utility of the presented computational approach to augment early candidate screening and later formulation strategies for protein therapeutics.

Utility of PBPK Modeling in Predicting and Characterizing Clinical Drug Interactions.

Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions. With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late stage drug development and is often utilized to support regulatory packages for new drug applications. This minireview will briefly describe the approaches to predicting DDI utilizing PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. Significance Statement PBPK models have been a key tool in the characterization of various pharmacokinetic phenomenon, including drug-drug interactions. This minireview will highlight recent advancements and publications around PBPK DDI modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

Public-private partnerships influencing the initiation and duration of clinical trials for neglected tropical diseases.

Public-private partnerships (PPPs) for neglected tropical diseases (NTDs) are often studied as an organizational form that facilitates the management and control of the huge costs of drug research and development. Especially the later stages of drug development, including clinical trials, become very expensive. This present study investigates whether and how the type of PPPs influences the initiation and duration of NTD clinical trials. Using the ClinicalTrials.gov database, a dataset of 1175 NTD clinical studies that started between 2000 and 2021 is analyzed based on affiliation information and project duration. For the NTD clinical trials that resulted from PPPs, the collaborating types were determined and analyzed, including the public sector-, private sector-, governmental sector-, and nongovernmental organization-led collaborations. The determinants for the discontinuation of all stopped clinical trials were categorized into scientific-, funding-, political-, and logistic dimensions. The results reveal that public sector-led PPPs were the most common collaborative types, and logistic and scientific issues were the most frequent determinants of stopped clinical trials. Trial registration: ClinicalTrials.gov.

Study design for development of novel safety biomarkers of drug-induced liver injury by the translational safety biomarker pipeline (TransBioLine) consortium: a study protocol for a nested case–control study

A lack of biomarkers that detect drug-induced liver injury (DILI) accurately continues to hinder early- and late-stage drug development and remains a challenge in clinical practice. The Innovative Medicines Initiative’s TransBioLine consortium comprising academic and industry partners is developing a prospective repository of deeply phenotyped cases and controls with biological samples during liver injury progression to facilitate biomarker discovery, evaluation, validation and qualification.In a nested case–control design, patients who meet one of these criteria, alanine transaminase (ALT) ≥ 5 × the upper limit of normal (ULN), alkaline phosphatase ≥ 2 × ULN or ALT ≥ 3 ULN with total bilirubin > 2 × ULN, are enrolled. After completed clinical investigations, Roussel Uclaf Causality Assessment and expert panel review are used to adjudicate episodes as DILI or alternative liver diseases (acute non-DILI controls). Two blood samples are taken: at recruitment and follow-up. Sample size is as follows: 300 cases of DILI and 130 acute non-DILI controls. Additional cross-sectional cohorts (1 visit) are as follows: Healthy volunteers (n = 120), controls with chronic alcohol-related or non-alcoholic fatty liver disease (n = 100 each) and patients with psoriasis or rheumatoid arthritis (n = 100, 50 treated with methotrexate) are enrolled. Candidate biomarkers prioritised for evaluation include osteopontin, glutamate dehydrogenase, cytokeratin-18 (full length and caspase cleaved), macrophage-colony-stimulating factor 1 receptor and high mobility group protein B1 as well as bile acids, sphingolipids and microRNAs. The TransBioLine project is enabling biomarker discovery and validation that could improve detection, diagnostic accuracy and prognostication of DILI in premarketing clinical trials and for clinical healthcare application.

CardioMotion: identification of functional and structural cardiotoxic liabilities in small molecules through brightfield kinetic imaging.

Cardiovascular toxicity is an important cause of drug failures in the later stages of drug development, early clinical safety assessment, and even postmarket withdrawals. Early-stage in vitro assessment of potential cardiovascular liabilities in the pharmaceutical industry involves assessment of interactions with cardiac ion channels, as well as induced pluripotent stem cell-derived cardiomyocyte-based functional assays, such as calcium flux and multielectrode-array assays. These methods are appropriate for the identification of acute functional cardiotoxicity but structural cardiotoxicity, which manifests effects after chronic exposure, is often only captured in vivo. CardioMotion is a novel, label-free, high throughput, in vitro assay and analysis pipeline which records and assesses the spontaneous beating of cardiomyocytes and identifies compounds which impact beating. This is achieved through the acquisition of brightfield images at a high framerate, combined with an optical flow-based python analysis pipeline which transforms the images into waveform data which are then parameterized. Validation of this assay with a large dataset showed that cardioactive compounds with diverse known direct functional and structural mechanisms-of-action on cardiomyocytes are identified (sensitivity = 72.9%), importantly, known structural cardiotoxins also disrupt cardiomyocyte beating (sensitivity = 86%) in this method. Furthermore, the CardioMotion method presents a high specificity of 82.5%.

Species comparison of compounds with known blood pressure effects in a vascular smooth muscle cell collagen contraction assay

IntroductionThere is a great need for new approaches early in drug discovery that have the potential to improve clinical translation of compound-mediated cardiovascular effects. Current approaches frequently rely on in vivo animal models or in vitro tissue bath preparations, both of which are low throughput and costly. An in vitro surrogate screen for blood pressure using primary human cells may serve as a higher throughput method to quickly select compounds void of this secondary pharmacology and potentially improve late-stage drug development outcomes. MethodsIn this study, we investigated 10 compounds with published in vivo blood pressure effects in a commercially available collagen contraction assay and evaluated rat, human, and canine (aortic) vascular smooth muscle cells (VSMCs). The aim of this study was to evaluate consistency between species and test their ability to predict the effects of known human vasodilators and constrictors. VSMCs were embedded at the same cell density in a collagen matrix which then floated freely in media containing test compounds. Collagen discs contracted faster than vehicle treated controls when incubated with a constrictor, and slower in the presence of a dilator. ResultsRat VSMCs responded as predicted of a VSMC-only culture to 9 out of 10 compounds. Human VSMCs responded as predicted to 8 out of 10 compounds, and canine VSMCs responded to 7 out of 10 compounds. DiscussionOur results suggest that rat VSMCs predict 90% of the effects of known vasoactive compounds in the collagen contraction assay while human and canine VSMCs were slightly less predictive (80% and 70%, respectively). Although blood pressure regulation is a multi-faceted and complex process, our data suggests the collagen smooth muscle contraction assay is useful as a qualitative early screen of compounds that act directly on smooth muscle cells of the arterial vasculature.

A Bayesian multi-arm multi-stage clinical trial design incorporating information about treatment ordering.

Multi-Arm Multi-Stage (MAMS) designs can notably improve efficiency in later stages of drug development, but they can be suboptimal when an order in the effects of the arms can be assumed. In this work, we propose a Bayesian multi-arm multi-stage trial design that selects all promising treatments with high probability and can efficiently incorporate information about the order in the treatment effects as well as incorporate prior knowledge on the treatments. A distinguishing feature of the proposed design is that it allows taking into account the uncertainty of the treatment effect order assumption and does not assume any parametric arm-response model. The design can provide control of the family-wise error rate under specific values of the control mean and we illustrate its operating characteristics in a study of symptomatic asthma. Via simulations, we compare the novel Bayesian design with frequentist multi-arm multi-stage designs and a frequentist order restricted design that does not account for the order uncertainty and demonstrate the gains in the sample sizes the proposed design can provide. We also find that the proposed design is robust to violations of the assumptions on the order.

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features.

Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein's amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the "OncologyTT" datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins' amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.

Abstract 971: Thymidine Kinase activity - A liquid biomarker reflecting proliferation rate and treatment efficacy in solid tumors provides unique guidance in oncology trials and drug development

Abstract Thymidine Kinase (TK) plays a fundamental role in DNA synthesis and cell proliferation. Expression of the enzyme is highly cell cycle dependent and correlates with cell growth state. TK activity (TKa) is present in all proliferating cells but release into blood circulation is low or absent from quiescent cells. Numerous studies have shown TKa to be elevated in solid tumors as well as in the blood of cancer patients with active disease. DiviTum® TKa, a clinically and analytically validated FDA cleared assay, can measure and quantify TKa in serum or plasma isolated from human and animal blood samples. TKa as a biomarker offers valuable information at all stages of drug development; from drug efficacy and dose response studies in cell culture and tumor xenograft models, to patient selection and treatment monitoring in clinical trials. Using TKa to measure the impact on cell proliferation as a drug evaluation endpoint can confirm mechanism of action efficacy and the minimally effective dose of a new compound rather than a maximally tolerated dose. In both cultured cells and mouse xenografts, a complete shutdown of tumor cell proliferation (assessed by TKa) can be observed well before and at much lower doses than when cell death or toxicity is used as the dose determination endpoint. The TKa-biomarker can also be used as a pharmacodynamic tool in clinical trials to improve study endpoints. Patient baseline levels (BL) of TKa are predictive/prognostic and can be used to identify which patients will respond to the therapy being tested vs those who will not. In metastatic breast cancer, a BL TKa value of <250 DuA is significantly correlated with longer PFS (HR=1.76, p<0.0001) and OS (HR=2.38, p<0.0001) independent of patient and disease characteristics. On-therapy TKa levels and dynamics can be used in clinical trials to evaluate response to a drug as early as two weeks into the first cycle. In patients receiving CDK4/6 inhibitors, TKa levels >140 DuA at cycle 1 day 15 predicts for lack of benefit to the drug (HR=5.65, p<0.0001). TKa can also be used as a predictive biomarker for drugs that cause an immune reaction and increase in cell proliferation such as immune checkpoint inhibitors (ICIs). When effective, ICIs cause an increase in immune cell proliferation which can be detected by elevated levels of TKa. Patients who do not achieve >1.5 fold change in TKa levels as compared to BL, have a significantly shorter duration of response to an ICI (HR=3.70, p=0.002) and patients with high TKa during ICI treatment have significantly longer OS (HR=3.49, p<0.05). In summary, TKa is a translational liquid biomarker that can bridge between preclinical and clinical studies, providing fundamental information for decision making during early and late stage drug development. Data and examples of the utility of TKa as a companion diagnostic for drug development will be presented. Citation Format: Mattias K. Bergqvist, Hanna Ritzén, Amy Williams. Thymidine Kinase activity - A liquid biomarker reflecting proliferation rate and treatment efficacy in solid tumors provides unique guidance in oncology trials and drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 971.

Characterization and validation of a middle-down hydrophobic interaction chromatography method to monitor methionine oxidation in IgG1

Post-translational modifications (PTMs) of therapeutic monoclonal antibodies (mAbs) can impact the efficacy of a drug. Methionine oxidation can alter the overall hydrophobicity of an antibody, thereby inducing conformational changes and affecting its biological activity. To ensure high quality, safety, and efficacy of mAbs, routine monitoring of PTMs such as methionine (Met) oxidation is essential. Met oxidation in the fragment crystallizable (Fc) region of immunoglobulin-G1 (IgG1) is a potential critical quality attribute because it impacts not only the interaction with the neonatal Fc receptor and protein A but also the half-life of mAbs in serum circulation. Although bottom-up mass spectrometry provides high site specificity, it may have limited application in quality control workflows, and its complicated sample preparation could result in procedure-induced oxidation. In this study, we describe the development and characterization of a rapid and robust middle-down hydrophobic interaction chromatography method for monitoring Met oxidation in the Fc region of IgG1. Additionally, we assessed a comprehensive method validation package and demonstrated the specificity, linearity, precision, and accuracy of the new method within a range of 3.8–37.7%. The relative quantitative data provided by this method may be used in a regulated workflow to support process and formulation development as well as in the later stages of drug development and batch release and stability studies.

Dose optimization during drug development: whether and when to optimize.

The goal of dose optimization during drug development is to identify a dose that preserves clinical benefit with optimal tolerability. Traditionally, the maximum tolerated dose in a small phase I dose escalation study is used in the phase II trial assessing clinical activity of the agent. Although it is possible that this dose level could be altered in the phase II trial if an unexpected level of toxicity is seen, no formal dose optimization has routinely been incorporated into later stages of drug development. Recently it has been suggested that formal dose optimization (involving randomly assigning patients between 2 or more dose levels) be routinely performed early in drug development, even before it is known that the experimental therapy has any clinical activity at any dose level. We consider the relative merits of performing dose optimization earlier vs later in the drug development process and demonstrate that a considerable number of patients may be exposed to ineffective therapies unless dose optimization is delayed until after clinical activity or benefit of the new agent has been established. We conclude that patient and public health interests may be better served by conducting dose optimization after (or during) phase III evaluation, with some exceptions when dose optimization should be performed after activity shown in phase II evaluation.

Optimal Enrollment in Late-Stage New Drug Development with Learning of Drug’s Efficacy for Group-Sequential Clinical Trials

Problem definition: The cost for developing a new drug ranged from $1 billion to more than $2 billion between 2010 and 2019. In addition to high development costs, the efficacy of the candidate drug, patient enrollment, the market exclusivity period (MEP), and the planning horizon are uncertain. Moreover, slow enrollment leads to increased costs, canceled clinical trials, and lost potential revenue. Many firms, hoping to detect efficacy versus futility of the candidate drug early to save development costs, plan interim analyses of patient-response data in their clinical trials. Academic/practical relevance: The problem for optimizing patient-enrollment rates has an uncertain planning horizon. We developed a continuous-time dynamic programming (DP) model with learning of a drug’s efficacy and MEP to assist firms in developing optimal enrollment policies in their clinical trials. We also established the optimality equation for this DP model. Through a clinical trial for testing a cancer drug developed by a leading pharmaceutical firm, we demonstrate that our DP model can help firms effectively manage their trials with a sizable profit gain (as large as $270 million per drug). Firms can also use our model in simulation to select their trial design parameters (e.g., the sample sizes of interim analyses). Methodology: We update a drug’s efficacy by Bayes’ rules. Using the stochastic order and the likelihood-ratio order of distribution functions, we prove the monotonic properties of the value function and an optimal policy. Results: We established that the value of the drug-development project increases as the average response from patients using the candidate drug increases. For drugs having low annual revenue or a strong market brand or treating rare diseases, we also established that the optimal enrollment policy is monotonic in the average patient response. Moreover, the optimal enrollment rate increases as the variance of the MEP decreases. Managerial implications: Firms can use the properties of the value function to select late-stage clinical trials for their drug-development project portfolios. Firms can also use our optimal policy to guide patient recruitment in their clinical trials considering competition from other drugs in the marketplace. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1162 .

Merck signs deals with Moderna, Royalty

Merck & Co. is paying and getting paid in two drug development deals. The company will pay Moderna $250 million to exercise an option to develop personalized cancer vaccines, including mRNA-4157/V940, which Moderna is evaluating in combination with Merck’s Keytruda immuno-oncology therapy in a Phase 2 trial. Separately, Royalty Pharma will pay Merck $50 million to cofund development of MK-8189, a small molecule in a Phase 2b study as a treatment for schizophrenia. Royalty buys drug royalties and also invests in late-stage drug development programs.

Identifying and Mitigating Potential Biases in Predicting Drug Approvals.

Machine learning models are increasingly applied to predict the drug development outcomes based on intermediary clinical trial results. A key challenge to this task is to address various forms of bias in the historical drug approval data. We aimed to identify and mitigate the bias in drug approval predictions and quantify the impacts of debiasing in terms of financial value and drug safety. We instantiated the Debiasing Variational Autoencoder, the state-of-the-art model for automated debiasing. We trained and evaluated the model on the Citeline dataset provided by Informa Pharma Intelligenceto predict the final drug development outcome from phase II trial results. The debiased Debiasing Variational Autoencoder model achieved better performance (measured by the [Formula: see text] score 0.48) in predicting the drug development outcomes than its un-debiased baseline ([Formula: see text] score 0.25). It had a much higher true-positive rate than baseline (60% vs 15%), while its true-negative rate was slightly lower (88% vs 99%). The Debiasing Variational Autoencoder distinguished between drugs developed by large pharmaceutical firms and those by small biotech companies. The model prediction is strongly influenced by multiple factors such as prior approval of the drug for another indication, whether the trial meets the positive/negative endpoints, and the year when the trial is completed. We estimate that the debiased model generates financial value for the drug developer in six major therapeutic areas, with a range of US$763-1,365 million. Our analysis shows that debiasing improves the financial efficiency of late-stage drug development. From the pharmacovigilance perspective, the debiased model is more likely to identify drugs that are both safe and effective. Meanwhile, it may predict a higher probability of success for drugs with potential adverse effects (because of its lower true-negative rate), thus it must be used with caution to predict the development outcomes of drug candidates currently in the pipeline.

Biomimetic <i>in vitro</i> heart platforms for drug development

New drug development is currently very expensive and time-consuming. In addition, some drugs that are approved after animal and clinical trials have their approval revoked because of adverse effects. About 30% of such drugs have heart side effects. Conventional cell-based drug toxicity tests are performed under conditions entirely different from the in vivo environment, and animal testing for drug evaluation has limitations because of differences between species. Therefore, researchers are increasingly focusing on developing models that can overcome these limitations to enable accurate drug toxicity tests. This review outlines biomimetic in vitro heart platforms, such as heart organoids, 3-dimensional bioprinting, and heart-on-a-chip devices, and describes their advantages, limitations, future perspectives. The development and use of effective cardiac biomimetic models could contribute to the development of alternatives to animal testing by providing more specific information on drug metabolism and reducing the rate of failure in later stages of drug development.

An order restricted multi-arm multi-stage clinical trial design.

One family of designs that can noticeably improve efficiency in later stages of drug development are multi-arm multi-stage (MAMS) designs. They allow several arms to be studied concurrently and gain efficiency by dropping poorly performing treatment arms during the trial aswell as by allowing to stop early for benefit. Conventional MAMS designs were developed for the setting, in which treatment arms are independent and hence can be inefficient when an order in the effects of the arms can be assumed (eg,when considering different treatment durations or different doses). In this work, we extend the MAMS framework to incorporate the order of treatment effects when no parametric dose-response or duration-responsemodel is assumed. The design can identify all promising treatments with high probability. We show that the design provides strong control of the family-wise error rate and illustrate the design in a study of symptomatic asthma. Via simulations we show that the inclusion of the ordering information leads to better decision-making compared to a fixed sample and aMAMS design. Specifically, in the considered settings, reductions in sample size of around 15% were achieved in comparison to a conventional MAMS design.

Improving the assessment of the probability of success in late stage drug development.

There are several steps to confirming the safety and efficacy of a new medicine. A sequence of trials, each with its own objectives, is usually required. Quantitative risk metrics can be useful for informing decisions about whether a medicine should transition from one stage of development to the next. To obtain an estimate of the probability of regulatory approval, pharmaceutical companies may start with industry-wide success rates and then apply to these subjective adjustments to reflect program-specific information. However, this approach lacks transparency and fails to make full use of data from previous clinical trials. We describe a quantitative Bayesian approach for calculating the probability of success (PoS) at the end of phase II which incorporates internal clinical data from one or more phase IIb studies, industry-wide success rates, and expert opinion or external data if needed. Using an example, we illustrate how PoS can be calculated accounting for differences between the phase II data and future phase III trials, and discuss how the methods can be extended to accommodate accelerated drug development pathways.

Organoid-Derived Epithelial Monolayer: A Clinically Relevant In Vitro Model for Intestinal Barrier Function

In the past, intestinal epithelial model systems were limited to transformed cell lines and primary tissue. These model systems have inherent limitations as the former do not faithfully represent original tissue physiology, and the availability of the latter is limited. Hence, their application hampers fundamental and drug development research. Adult stem-cell-based organoids (henceforth referred to as organoids) are miniatures of normal or diseased epithelial tissue from which they are derived. They can be established very efficiently from different gastrointestinal (GI) tract regions, have long-term expandability, and simulate tissue- and patient-specific responses to treatments in vitro. Here, the establishment of intestinal organoid-derived epithelial monolayers has been demonstrated along with methods to measure epithelial barrier integrity, permeability and transport, antimicrobial protein secretion, as well as histology. Moreover, intestinal organoid-derived monolayers can be enriched with proliferating stem and transit-amplifying cells as well as with key differentiated epithelial cells. Therefore, they represent a model system that can be tailored to study the effects of compounds on target cells and their mode of action. Although organoid cultures are technically more demanding than cell lines, once established, they can reduce failures in the later stages of drug development as they truly represent in vivo epithelium complexity and interpatient heterogeneity.

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).