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Trials seeking to delay or prevent type 1 diabetes (T1D) continue to face multiple challenges, including both clinical endpoint (s) and cohort selection. While it is clear that individuals who possess islet cell autoantibodies (AAbs) are at elevated risk for developing T1D, it remains unclear as to what additional factors, including metabolic activities and anthropometric data, may contribute to this increased risk. The aim of this project was to inform T1D prevention trial designs by developing a quantitative joint model based-clinical trial simulation (CTS) tool. Individual-level data obtained from the NIH TEDDY and TrialNet TNnatural history studies were used in this tool’s development. To describe T1D disease progression quantitatively, both longitudinal and time-to-event variables were simultaneously quantified as a joint model, with results from a 2-hour oral glucose tolerance test (OGTT) assessed at each visit included as a time-varying longitudinal covariate affecting the T1D diagnosis event. Our study focused on individuals at risk of developing T1D with two or more AAbs. Among the investigated individuals, those with a high BMI and the presence of GADA AAb at baseline had less risk, while those with an elevated HbA1c value, high fasting OGTT value, increased fasting OGTT to 2-hour OGTT ratio, and the presence of the IA2A AAb at baseline had higher risk. The developed model based-CTS tool, where users can specify the trial design, participants’ characteristics and hypothetical drug effect, provides a framework to perform T1D prevention clinical trial simulations with the intention to assist in future trial design decision-making processes. Disclosure J.F. Morales: None. R. Muse: None. J. Podichetty: None. S. David: None. M.A. Atkinson: None. M.J. Haller: Advisory Panel; SAB Biotherapeutics. Consultant; MannKind Corporation, Sanofi. M. Campbell-Thompson: Consultant; TRex Bio. K. Romero: None. I. O'Doherty: None. S. Schmidt: None. S. Kim: None. Funding JDRF (grant #: 2-SRA-2020-903-A-N) ; NIDDK Central Repositories

Interest in drug development for rare diseases has expanded dramatically since the Orphan Drug Act was passed in 1983, with 40% of new drug approvals in 2019 targeting orphan indications. However, limited quantitative understanding of natural history and disease progression hinders progress and increases the risks associated with rare disease drug development. Use of international data standards can assist in data harmonization and enable data exchange, integration into larger datasets, and a quantitative understanding of disease natural history. The US Food and Drug Administration (FDA) requires the use of Clinical Data Interchange Consortium (CDISC) Standards in new drug submissions to help the agency efficiently and effectively receive, process, review, and archive submissions, as well as to help integrate data to answer research questions. Such databases have been at the core of biomarker qualification efforts and fit‐for‐purpose models endorsed by the regulators. We describe the development of CDISC therapeutic area user guides for Duchenne muscular dystrophy and Huntington’s disease through Critical Path Institute consortia. These guides describe formalized data structures and controlled terminology to map and integrate data from different sources. This will result in increased standardization of data collection and allow integration and comparison of data from multiple studies. Integration of multiple data sets enables a quantitative understanding of disease progression, which can help overcome common challenges in clinical trial design in these and other rare diseases. Ultimately, clinical data standardization will lead to a faster path to regulatory approval of urgently needed new therapies for patients.

Efforts for sharing individual clinical data are gaining momentum due to a heightened recognition that integrated data sets can catalyze biomedical discoveries and drug development. Among the benefits are the fact that data sharing can help generate and investigate new research hypothesis beyond those explored in the original study. Despite several accomplishments establishing public systems and guidance for data sharing in clinical trials, this practice is not the norm. Among the reasons are ethical challenges, such as privacy of individuals, data ownership, and control. This paper creates awareness of the potential benefits and challenges of sharing individual clinical data, how to overcome these challenges, and how as a clinical pharmacology community we can shape future directions in this field.

Our goal was to assess the enrichment utility of hippocampal volume (HV) as an enrichment biomarker in amnestic mild cognitive impairment (aMCI) clinical trials, and, hence, develop an HV neuroimaging-informed clinical trial enrichment tool. Modeling of integrated longitudinal patient-level data came from open-access natural history studies in patients diagnosed with aMCI-the Alzheimer's Disease Neuroimaging Initiative (ADNI)-1 and ADNI-2-and indicated that a decrease of 1 cm3 with respect to the analysis dataset median baseline intracranial volume-adjusted HV (ICV-HV; ~ 5 cm3 ) is associated with > 50% increase in disease progression rate as measured by the Clinical Dementia Rating Scale-Sum of Boxes. Clinical trial simulations showed that the inclusion of aMCI subjects with baseline ICV-HV below the 84th or 50th percentile allowed an approximate reduction in trial size of at least 26% and 55%, respectively. This clinical trial enrichment tool can help design more efficient and informative clinical trials.

Contains fulltext : 229477.pdf (Publisher’s version ) (Open Access) Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

Rare diseases impact the lives of an estimated 350 million people worldwide, and yet about 90% of rare diseases remain without an approved treatment. New technologies have become available, such as gene and oligonucleotide therapies, that offer great promise in treating rare diseases. However, progress toward the development of therapies to treat these diseases is hampered by a limited understanding of the course of each rare disease, how changes in disease progression occur and can be effectively measured over time, and challenges in designing and running clinical trials in diseases where the natural history is poorly characterized. Data that could be used to characterize the natural history of each disease has often been collected in various ways, including in electronic health records, patient-report registries, clinical natural history studies, and in past clinical trials. However, each data source contains a limited number of subjects and different data elements, and data is frequently kept proprietary in the hands of the study sponsor rather than shared widely across the rare disease community. The Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP) is an FDA-funded effort to overcome these persistent challenges. By aggregating data across all rare diseases and making that data available to the community to support understanding of rare disease natural history and inform drug development, RDCA-DAP aims to accelerate the regulatory approval of new therapies. RDCA-DAP curates, standardizes, and tags data across rare disease datasets to make it findable within the database, and contains a built-in analytics platform to help visualize, interpret, and use it to support drug development. RDCA-DAP will coordinate data and tool resources across non-profit, commercial, and for-profit entities to serve a diverse array of rare disease stakeholders that includes academic researchers, drug developers, FDA reviewers and of course patients and their caregivers. Drug development programs utilizing the RDCA-DAP will be able to leverage existing data to support their efforts and reach definitive decisions on the efficacy of their therapeutics more efficiently and more rapidly than ever.

Early clinical trials of therapies to treat Duchenne muscular dystrophy (DMD), a fatal genetic X-linked pediatric disease, have been designed based on the limited understanding of natural disease progression and variability in clinical measures over different stages of the continuum of the disease. The objective was to inform the design of DMD clinical trials by developing a disease progression model-based clinical trial simulation (CTS) platform based on measures commonly used in DMD trials. Data were integrated from past studies through the Duchenne Regulatory Science Consortium founded by the Critical Path Institute (15 clinical trials and studies, 1505 subjects, 27,252 observations). Using a nonlinear mixed-effects modeling approach, longitudinal dynamics of five measures were modeled (NorthStar Ambulatory Assessment, forced vital capacity, and the velocities of the following three timed functional tests: time to stand from supine, time to climb 4 stairs, and 10 meter walk-run time). The models were validated on external data sets and captured longitudinal changes in the five measures well, including both early disease when function improves as a result of growth and development and the decline in function in later stages. The models can be used in the CTS platform to perform trial simulations to optimize the selection of inclusion/exclusion criteria, selection of measures, and other trial parameters. The data sets and models have been reviewed by the US Food and Drug Administration and the European Medicines Agency; have been accepted into the Fit-for-Purpose and Qualification for Novel Methodologies pathways, respectively; and will be submitted for potential endorsement by both agencies.

A quantitative assessment of Parkinson’s disease (PD) progression is critical for optimizing clinical trials design. Disease progression model was developed using pooled data from the Progression Marker Initiative study and the Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation in Parkinson’s Disease study. Age, gender, concomitant medication, and study arms were predictors of baseline. A mutation in the leucine-rich repeat kinase 2 (LRRK2) encoding gene was associated with the disease progression rate. The progression rate in subjects with PD who carried LRRK2 mutation was slightly slower (~0.170 points/month) than that in PD subjects without the mutation (~0.222 points/month). For a non-enriched placebo-controlled clinical trial, approximately 70 subjects/arm would be required to detect a drug effect of 50% reduction in the progression rate with 80% probability. Whereas, 85, 93 and 100 subjects/arm would be required for an enriched clinical trial with 30%, 50% and 70% subjects with LRRK2 mutations, respectively.

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase., Laboratorio de Investigación y Desarrollo de Bioactivos

Antibody-drug conjugates (ADCs) are cancer drugs composed of a humanized antibody linked to a cytotoxic payload, allowing preferential release of payload in cancer cells expressing the antibody-targeted antigen. Here, a systems pharmacology model is used to simulate ADC transport from blood to tumor tissue and ADC uptake by tumor cells. The model includes effects of spatial gradients in drug concentration in a three-dimensional network of tumor blood vessels with realistic geometry, and accounts for diffusion of ADC in the tumor extracellular space, binding to antigen, internalization, intracellular processing, and payload efflux from cells. Cells that process an internalized ADC-antigen complex may release payload that can be taken up by other “bystander” cells. Such bystander effects are included in the model. The model is used to simulate conditions in previous experiments, showing good agreement with experimental results. Simulations are used to analyze the relationship of bystander effects to payload properties and single dose administrations. The model indicates that exposure of payload to cells distant from vessels is sensitive to the free payload diffusivity in the extracellular space. When antigen expression is heterogeneous, the model indicates that the amount of payload accumulating in non-antigen expressing cells increases linearly with dose but depends only weakly on the percentage of antigen expressing cells. The model provides an integrated mechanistic framework for understanding the effects of spatial gradients on drug distribution using ADCs and for designing ADCs to achieve more effective payload distribution in solid tumors, thereby increasing the therapeutic index of the ADC.

Alzheimer's disease (AD) is the leading cause of dementia worldwide. With 35 million people over 60 years of age with dementia, there is an urgent need to develop new treatments for AD. To streamline this process, it is imperative to apply insights and learnings from past failures to future drug development programs. In the present work, we focus on how modeling and simulation tools can leverage open data to address drug development challenges in AD.

Time to RECIST progression (TTP) is currently used as a surrogate for patient benefit for either accelerated regulatory approval or to study the effects of factors such as drug exposure and drug target expression levels on RECIST progression-free survival (PFS). While the conventional paradigm assumes that increased antitumor effect results in a longer TTP and PFS, we show that this is often not the case due to a fundamental mathematical property of the RECIST definition of disease progression in which faster tumor shrinkage rate (k) can lead to paradoxically shorter TTP (1,2). We investigate under what conditions this paradoxical behavior occurs, what fraction of patients satisfy these conditions, and whether this phenomenon can invert population-level PFS hazard ratio. We use a mathematical model treating tumor burden as decreasing drug-sensitive and increasing drug-resistant cell subpopulations and fit this model to data from several clinical trials with different indications (3). We simulate a more effective treatment and record the resulting TTP. A study-level analysis compares the relationship of speed and depth of response with TTP20 for both the administered “control” and simulated “more effective” drug. Depending on indication, 3-27% of patients are estimated to have a paradoxically inverse relationship between k and TTP. We conclude that while RECIST progression criteria may be useful for decision making in patients, care must be taken in interpreting TTP as a metric of treatment efficacy due to the real possibility that a stronger treatment effect paradoxically results in shorter TTP. We propose and test an alternative benefit metric: the model-projected time that tumor size reaches 120% of baseline (TTB120). Simulated head-to-head studies show that in contrast to TTP, TTB120 is always consistent with patient benefit. We recommend that any alternative metrics with a time-to-event endpoint should consider the directional consistency between speed and depth of response that the endpoint is consistent with patient benefit under any set of patient conditions. References: 1. Eisenhauer EA et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer 2009;45(2):228-47. 2. Johnson K et al. Directional inconsistency between Response Evaluation Criteria in Solid Tumors (RECIST) time to progression and response speed and depth. Eur J Cancer 2019;109:196-203. 3. Project Data Sphere, https://www.projectdatasphere.org/projectdatasphere/html/home; 2018 [accessed 7 September 2018]. Citation Format: Kaitlyn E. Johnson, Axel Gomez, Jackson Burton, Douglas White, Arijit Chakravarty, Annette Schmid, Dean Bottino. An alternative time-to-event metric that is consistent with tumor response speed and depth, unlike RECIST time to progression [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 11.

Aim We seek to characterize how faster tumour shrinkage rate (k) can lead to paradoxically shorter Response Evaluation Criteria in Solid Tumors (RECIST) time to progression (‘TTP20’ – tumour size exceeding its minimum by 5 mm and 20%) [1] and, therefore, progression-free survival (PFS). Specifically, we investigate under what conditions this paradoxical behaviour occurs, what fraction of patients satisfy these conditions, whether this phenomenon can invert population-level PFS hazard ratio, and consistency of an alternative time-to-event benefit metric with k. Methods We use a mathematical model treating tumour burden as decreasing drug-sensitive and increasing drug-resistant cell subpopulations. We fit this model to data from several clinical trials with different indications [2]. We simulated a more effective treatment and recorded whether patients' TTP20 increased or decreased. We performed a study-level analysis to compare the relationship of speed and depth of response with TTP20 for both the administered ‘control’ and simulated ‘more effective’ drug. We propose and test an alternative benefit metric: the model-projected time that tumour size reaches 120% of baseline (TTB120). Results Depending on indication, 3–27% of patients are estimated to have a paradoxically inverse relationship between k and TTP20. Simulated head-to-head studies show that TTP20-based PFS can favour the less effective drug. In contrast, TTB120 always favours the more effective drug. Conclusion We demonstrate the paradoxical behaviour of RECIST TTP20 – as an exemplar of percent-change-from-nadir based cancer progression criterion – both in theory and in observed patient data at the individual and trial level. We propose an alternative tumour size–based criterion (TTB120) that is directionally consistent with tumour shrinkage rate.

We consider the interplay of vaccination and migration rates on disease persistence in epidemiological systems. We show that short-term and long-term migration can inhibit disease persistence. As a result, we show how migration changes how vaccination rates should be chosen to maintain herd immunity. In a system of coupled SIR models, we analyze how disease eradication depends explicitly on vaccine distribution and migration connectivity. The analysis suggests potentially novel vaccination policies that underscore the importance of optimal placement of finite resources.

e14022 Background: RECIST time to progression (RTTP) is the cornerstone of widely used surrogates for patient benefit such as RECIST progression-free survival (RPFS), which in turn is used either for accelerated regulatory approval or to study the effects of factors such as drug exposure and drug target expression levels on RPFS. While one might expect increased antitumor effect to result in longer RTTP and RPFS, we will show that this is often not the case due to a fundamental mathematical property of the RECIST definition of disease progression. Methods:We use a simple mathematical model that treats patient tumor burden dynamics as an evolutionary process consisting of a decreasing drug-sensitive subpopulation of tumor cells and an independently growing drug-resistant subpopulation. This model has successfully described tumor dynamics in NSCLC, melanoma (CPT Pharmacometrics Syst. Pharmacol. (2017) 6, 29–39), and ovarian cancer (internal data). In the presence of drug, we represent the net fitness of the sensitive cells in terms of an exponential decay or “kill” rate k > 0, and the net fitness of the resistant cells as an exponential growth rate g > 0, and the initial fraction of drug-resistant cells by 0 ≤ f ≤ 1. For a given virtual patient (parameter set), we simulate RTTP as the time after start of treatment at which tumor burden V(t) is 20% higher than its lowest value (per RECIST criteria). We then vary the kill rate k and check whether RTTP increases or decreases with increasing k. Results: There is a large set of parameter values for which RTTP is predicted to vary inversely with increasing kill rate k. These parameter values are attained clinically in ovarian cancer patients taking alisertib and paclitaxel (internal data). Conclusions: While RECIST progression criteria may be useful for patient care to identify when a patient is losing her response, care must be taken in interpreting RTTP as a metric of treatment effect due to the real possibility that a stronger treatment effect results in shorter RTTP. We recommend that alternative metrics for patient benefit, such as a generalization of Days Gained (Neal et al PLoS ONE 8(1)e51951), be evaluated. We will present results of these evaluations currently in progress.

Introduction What is the optimal “shape” for a drug pipeline: A flute? A megaphone? A trumpet? In other words, what set of stringencies for passage of candidates to the next stage in development maximizes the value of the pipeline as a whole, and how do assays predicting drug quality influence this optimum? Methods A linear programming formulation was developed for four phases of a drug pipeline: Preclinical, Phase I, Phase II, and Phase III, with variables including: the total budget for the entire pipeline (B), and the number (Ni), cost (ci), and net present value (vi) of drugs in each phase (i). The model is subject to two constraints: (1) the pipeline must reduce in size with each subsequent phase (i.e. Npre≥ N1 ≥ N2 ≥ N3), and (2) the sum total of the cost times the number of drugs in each phase must be less than or equal to the total budget (i.e. cpreNpre + c1N1 + c2N2 + c3N3 ≤ B). The overall goal is to maximize the total value of drugs progressing through the pipeline (i.e. maximize vtot = vpreNpre + v1N1 + v2N2 + v3N3). Additionally, pipeline evolution was modeled to evaluate the implications of a “predictive” drug quality assay. Hypothetical “drug quality scores” were drawn from normal distributions which reflect industry probabilities of success between phases. Drugs that are above a cutoff stringency advance forward, with the process repeated for all phases. The cost of each phase and value for successful drugs are recorded for each simulation. The process is repeated many times, with the expected value calculated at each iteration. Results Beginning with a pool of drug candidates, the optimal pipeline structure should approach equal sized pipeline widths as rapidly as possible. Monte Carlo simulations demonstrated that increasing the stringency of a “quality assay” in Phase I results in a linear increase in expected value. Additionally, if the quality test is improved, e.g. by reducing the variability of the assay, the improvement in expected value will increase in an exponential manner. Conclusions The simulations predict that to maximize pipeline value, drug companies should: (a) pick the winners as early as possible (i.e. Preclinical or Phase I), (b) set stringent go/no-go criteria in Preclinical and Phase I development, and (c) perform prospective clinical trial simulations (Preclinical to Phase I, Phase I to Phase II, Phase II to Phase III) to improve the ability to predict winners. Citation Format: Jackson Burton, William Antebi, Christopher J. Zopf, Dean Bottino, Shu-Wen Teng, Ryan Nolan, Arijit Chakravarty. A model-based approach toward clinical pipeline optimization. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1636. doi:10.1158/1538-7445.AM2015-1636

Introduction The “bystander effect” is the hypothesized process by which neighboring cancer cells are killed when free drug is released into the tumor environment by dying cells. The extent to which an antibody drug conjugate (ADC) elicits the bystander effect can have significant impact on the magnitude and dynamics of efficacy. Understanding the bystander effect though experimental methods (e.g. video-microscopy) are challenging; however, when supplemented with in silico modeling, mechanistic details can be elucidated. The purpose of this work is to develop a spatial-temporal in silico platform describing tumor cell progression in the presence of an ADC in order to quantify the bystander effect. Methods The model employs spatial-stochastic methods to simulate the time evolution of cell death resulting from ADCs and freely diffusing payload based on following parameters: drug to antibody ratio (DAR), linker stability, antigen concentration, ADC cell membrane permeability, ADC-antigen internalization, payload permeability, and payload potency. Specifically, a 2-D rectangular grid of tumor cells, ADCs, and payload are numerically implemented through binary pixel occupation of the grid. Governing rules are then assigned for: mobility for freely diffusing ADCs and payload, reaction rate for ADC binding to antigen-expressing cells, rate of cell death dependent on payload concentration, and internalization rate of payload into non-antigen expressing cells. The simulation is run until stopping criteria are met, e.g. all cells are killed. Baseline parameters can be optimized by fitting the model to in vitro experimental video-microscopy data. Results Experimental conditions were simulated with the model to characterize the influence of the aforementioned ADC and biological parameters on the bystander effect. Conclusions By incorporating the spatial aspect of ADC mechanics, spatially dependent metrics can be formulated to quantify the bystander effect. The model allows for wide adjustability of the ADC design parameters as well as relevant biological parameters. Thus, by understanding the relative parameter sensitivities, the model can explore design optimization of ADCs that maximize the bystander effect. Through such optimization, the magnitude and dynamics of drug exposure at the site of action can be controlled to maximize efficacy and improve the therapeutic window. Citation Format: Jackson Burton, Shu-Wen Teng, Christopher J. Zopf, Ryan Nolan, Arijit Chakravarty. An in silico platform for characterizing ADC bystander effects. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5337. doi:10.1158/1538-7445.AM2015-5337