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Clinical trials seeking to delay or prevent the onset of type 1 diabetes (T1D) face a series of pragmatic challenges. Despite more than 100 years since the discovery of insulin, teplizumab remains the only FDA‐approved therapy to delay progression from Stage 2 to Stage 3 T1D. To increase the efficiency of clinical trials seeking this goal, our project sought to inform T1D clinical trial designs by developing a disease progression model‐based clinical trial simulation tool. Using individual‐level data collected from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies, we previously developed a quantitative joint model to predict the time to T1D onset. We then applied trial‐specific inclusion/exclusion criteria, sample sizes in treatment and placebo arms, trial duration, assessment interval, and dropout rate. We implemented a function for presumed drug effects. To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. As an output, power was calculated, which summarizes the probability of success, showing a statistically significant difference in the time distribution until the T1D diagnosis between the two arms. Using this tool, power curves can also be generated through iterations. The web‐based tool is publicly available: https://app.cop.ufl.edu/t1d/. Herein, we briefly describe the tool and provide instructions for simulating a planned clinical trial with two case studies. This tool will allow for improved clinical trial designs and accelerate efforts seeking to prevent or delay the onset of T1D. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Whereas islet autoantibodies (AAs) are well‐established risk factors for developing type 1 diabetes (T1D), there is a lack of biomarkers endorsed by regulators to enrich clinical trial populations for those at risk of developing T1D. As such, the development of therapies that delay or prevent the onset of T1D remains challenging. To address this drug development need, the Critical Path Institute's T1D Consortium (T1DC) acquired patient‐level data from multiple observational studies and used a model‐based approach to evaluate the utility of islet AAs as enrichment biomarkers in clinical trials. An accelerated failure time model was developed, discussed in our previous publication, which provided the underlying evidence required to receive a qualification opinion for islet AAs as enrichment biomarkers from the European Medicines Agency (EMA) in March 2022. To further democratize the use of the model for scientists and clinicians, we developed a Clinical Trial Enrichment Graphical User Interface. The interactive tool allows users to specify trial participant characteristics, including the percentage of participants with a specific AA combination. Users can specify ranges for participant baseline age, sex, blood glucose measurement from the 120‐minute timepoints of an oral glucose tolerance test, and HbA1c. The tool then applies the model to predict the mean probability of a T1D diagnosis for that trial population and renders the results to the user. To ensure adequate data privacy and to make the tool open‐source, a deep learning‐based generative model was used to generate a cohort of synthetic subjects that underpins the tool. [ABSTRACT FROM AUTHOR]

Clinical trials seeking type 1 diabetes prevention are challenging in terms of identifying patient populations likely to progress to type 1 diabetes within limited (i.e., short‐term) trial durations. Hence, we sought to improve such efforts by developing a quantitative disease progression model for type 1 diabetes. Individual‐level data obtained from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies were used to develop a joint model that links the longitudinal glycemic measure to the timing of type 1 diabetes diagnosis. Baseline covariates were assessed using a stepwise covariate modeling approach. Our study focused on individuals at risk of developing type 1 diabetes with the presence of two or more diabetes‐related autoantibodies (AAbs). The developed model successfully quantified how patient features measured at baseline, including HbA1c and the presence of different AAbs, alter the timing of type 1 diabetes diagnosis with reasonable accuracy and precision (<30% RSE). In addition, selected covariates were statistically significant (p < 0.0001 Wald test). The Weibull model best captured the timing to type 1 diabetes diagnosis. The 2‐h oral glucose tolerance values assessed at each visit were included as a time‐varying biomarker, which was best quantified using the sigmoid maximum effect function. This model provides a framework to quantitatively predict and simulate the time to type 1 diabetes diagnosis in individuals at risk of developing the disease and thus, aligns with the needs of pharmaceutical companies and scientists seeking to advance therapies aimed at interdicting the disease process. [ABSTRACT FROM AUTHOR]

The development of therapies to prevent or delay the onset of type 1 diabetes (T1D) remains challenging, and there is a lack of qualified biomarkers to identify individuals at risk of developing T1D or to quantify the time‐varying risk of conversion to a diagnosis of T1D. To address this drug development need, the T1D Consortium (i) acquired, remapped, integrated, and curated existing patient‐level data from relevant observational studies, and (ii) used a model‐based approach to evaluate the utility of islet autoantibodies (AAs) against insulin/proinsulin autoantibody, GAD65, IA‐2, and ZnT8 as biomarkers to enrich subjects for T1D prevention. The aggregated dataset was used to construct an accelerated failure time model for predicting T1D diagnosis. The model quantifies presence of islet AA permutations as statistically significant predictors of the time‐varying probability of conversion to a diagnosis of T1D. Additional sources of variability that greatly improved the accuracy of quantifying the time‐varying probability of conversion to a T1D diagnosis included baseline age, sex, blood glucose measurements from the 120‐minute timepoints of oral glucose tolerance tests, and hemoglobin A1c. The developed models represented the underlying evidence to qualify islet AAs as enrichment biomarkers through the qualification of novel methodologies for drug development pathway at the European Medicines Agency (EMA). Additionally, the models are intended as the foundation of a fully functioning end‐user tool that will allow sponsors to optimize enrichment criteria for clinical trials in T1D prevention studies. [ABSTRACT FROM AUTHOR]

Study Objective: Basiliximab is an immunosuppressive monoclonal antibody used for rejection prevention following solid organ transplantation; the pharmacokinetics (PK) of basiliximab in this setting are known. Basiliximab may also be used for prophylaxis and treatment of graft‐versus‐host disease (GVHD) in patients undergoing allogeneic hematopoietic cell transplantation (HCT); however, the PK of basiliximab in this setting are not known. Clinical transplant providers expect variation in the volume of distribution and clearance after nonmyeloablative allogeneic transplantation (NMAT) compared with solid organ transplantation. Blood loss, organ site–specific antibody accumulation, and differences in blood product use during the two transplantation approaches may generate differences in basiliximab PK. Therefore, the objective of this study was to describe the PK of basiliximab after its addition to a minimally intense NMAT regimen, in conjunction with cyclosporine, for GVHD prophylaxis in patients with hematologic malignancies. Design: Population PK analysis of a single‐center, single‐arm, phase II clinical trial. Setting: Academic cancer research center. Patients: Fourteen adults with hematologic malignancies (acute myeloid leukemia, acute lymphoblastic leukemia, chronic lymphocytic leukemia, myelodysplastic syndrome, non‐Hodgkin's lymphoma, Hodgkin's lymphoma, myelofibrosis, or severe aplastic anemia) and undergoing NMAT with a fully HLA‐matched (10 of 10 antigen matched) related or unrelated donor. Measurements and Main Results: Basiliximab was used in conjunction with cyclosporine to deplete activated T cells in vivo as GVHD prophylaxis. We developed a novel competitive enzyme‐linked immunosorbent assay (ELISA) method using recombinant interleukin‐2 receptor alpha‐chain (IL‐2Ra) and a commercially available soluble sIL‐2R ELISA kit to permit the quantification of serum basiliximab concentrations and characterization of the PK properties of the drug in this patient population. Using a nonlinear mixed effects model with NONMEM software, a one‐compartment model with first‐order elimination best described the PK, as covariate analysis using stepwise covariate modeling did not improve the base model. Conclusion: We suggest a one‐compartment population model with first‐order elimination to capture the PK profile for basiliximab for this patient population. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, the distribution of oxygen and glucose was evaluated along with consumption by hepatocytes using three different approaches. The methods include (i) Computational Fluid Dynamics (CFD) simulation, (ii) residence time distribution (RTD) analysis using a step-input coupled with segregation model or dispersion model, and (iii) experimentally determined consumption by HepG2 cells in an open-loop. Chitosan-gelatin (CG) scaffolds prepared by freeze-drying and polycaprolactone (PCL) scaffolds prepared by salt leaching technique were utilized for RTD analyses. The scaffold characteristics were used in CFD simulations i.e. Brinkman's equation for flow through porous medium, structural mechanics for fluid induced scaffold deformation, and advection-diffusion equation coupled with Michaelis-Menten rate equations for nutrient consumption. With the assumption that each hepatocyte behaves like a micro-batch reactor within the scaffold, segregation model was combined with RTD to determine exit concentration. A flow rate of 1 mL/min was used in the bioreactor seeded with 0.6 × 106 HepG2 cells/cm3 on CG scaffolds and oxygen consumption was measured using two flow-through electrodes located at the inlet and outlet. Glucose in the spent growth medium was also analyzed. RTD results showed distribution of nutrients to depend on the surface characteristics of scaffolds. Comparisons of outlet oxygen concentrations between the simulation results, and experimental results showed good agreement with the dispersion model. Outlet oxygen concentrations from segregation model predictions were lower. Doubling the cell density showed a need for increasing the flow rate in CFD simulations. This integrated approach provide a useful strategy in designing bioreactors and monitoring tissue regeneration. Biotechnol. Bioeng. 2015;112: 393-404. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In this study, we tested the possibility of calculating permeability of porous scaffolds utilized in soft tissue engineering using pore size and shape. We validated the results using experimental measured pressure drop and simulations with the inclusion of structural deformation. We prepared Polycaprolactone (PCL) and Chitosan-Gelatin (CG) scaffolds by salt leaching and freeze drying technique, respectively. Micrographs were assessed for pore characteristics and mechanical properties. Porosity for both scaffolds was nearly same but the permeability varied 10-fold. Elastic moduli were 600 and 9 kPa for PCL and CG scaffolds, respectively, while Poisson's ratio was 0.3 for PCL scaffolds and ∼1.0 for CG scaffolds. A flow-through bioreactor accommodating a 10 cm diameter and 0.2 cm thick scaffold was used to determine the pressure-drop at various flow rates. Additionally, computational fluid dynamic (CFD) simulations were performed by coupling fluid flow, described by Brinkman equation, with structural mechanics using a dynamic mesh. The experimentally obtained pressure drop matched the simulation results of PCL scaffolds. Simulations were extended to a broad range of permeabilities (10−10 m2 to 10−14 m2), elastic moduli (10-100,000 kPa) and Poisson's ratio (0.1-0.49). The results showed significant deviation in pressure drop due to scaffold deformation compared to rigid scaffold at permeabilities near healthy tissues. Also, considering the scaffold as a nonrigid structure altered the shear stress profile. In summary, scaffold permeability can be calculated using scaffold pore characteristics and deformation could be predicted using CFD simulation. These relationships could potentially be used in monitoring tissue regeneration noninvasively via pressure drop. © 2013 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 102B: 737-748, 2014. [ABSTRACT FROM AUTHOR]

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials. Although the current efficacy failure endpoint has typically shown the noninferiority of therapeutic regimens, the iBOX Scoring System can be used to demonstrate the superiority of a new immunosuppressive therapy compared to the standard of care from 6 months to 24 months posttransplant in pivotal or exploratory drug therapeutic studies., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)