Artificial Intelligence for In Silico Clinical Trials: A Review

A clinical trial is an essential step in drug development, which is often costly and time-consuming. In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials. AI-enabled in silico trials can increase the case group size by creating virtual cohorts as controls. In addition, it also enables automation and optimization of trial design and predicts the trial success rate. This article systematically reviews papers under three main topics: clinical simulation, individualized predictive modeling, and computer-aided trial design. We focus on how machine learning (ML) may be applied in these applications. In particular, we present the machine learning problem formulation and available data sources for each task. We end with discussing the challenges and opportunities of AI for in silico trials in real-world applications

Standardizing Clinical Trials Workflow Representation in UML for International Site Comparison

BACKGROUND: With the globalization of clinical trials, a growing emphasis has been placed on the standardization of the workflow in order to ensure the reproducibility and reliability of the overall trial. Despite the importance of workflow evaluation, to our knowledge no previous studies have attempted to adapt existing modeling languages to standardize the representation of clinical trials. Unified Modeling Language (UML) is a computational language that can be used to model operational workflow, and a UML profile can be developed to standardize UML models within a given domain. This paper's objective is to develop a UML profile to extend the UML Activity Diagram schema into the clinical trials domain, defining a standard representation for clinical trial workflow diagrams in UML. METHODS: Two Brazilian clinical trial sites in rheumatology and oncology were examined to model their workflow and collect time-motion data. UML modeling was conducted in Eclipse, and a UML profile was developed to incorporate information used in discrete event simulation software. RESULTS: Ethnographic observation revealed bottlenecks in workflow: these included tasks requiring full commitment of CRCs, transferring notes from paper to computers, deviations from standard operating procedures, and conflicts between different IT systems. Time-motion analysis revealed that nurses' activities took up the most time in the workflow and contained a high frequency of shorter duration activities. Administrative assistants performed more activities near the beginning and end of the workflow. Overall, clinical trial tasks had a greater frequency than clinic routines or other general activities. CONCLUSIONS: This paper describes a method for modeling clinical trial workflow in UML and standardizing these workflow diagrams through a UML profile. In the increasingly global environment of clinical trials, the standardization of workflow modeling is a necessary precursor to conducting a comparative analysis of international clinical trials workflows

Focused model selection in quantile regression

We consider the problem of model selection for quantile regression analysis where a particular purpose of the modeling procedure has to be taken into account. Typical examples include estimation of the area under the curve in pharmacokinetics or estimation of the minimum effective dose in phase II clinical trials. A focused information criterion for quantile regression is developed, analyzed and investigated by means of a simulation study

Quantitative systems pharmacology modeling of biotherapeutics in oncology

In this thesis, mathematical modeling and simulation was applied as a tool to inform quantitative decision making in oncology drug discovery and development. Modeling based approaches were shown to be useful to understand the mechanism of action and deconvolve the complexities of novel biotherapeutic modalities being used to treat cancer, including monospecific and bispecific monoclonal antibodies and antibody drug conjugates. Several key observations and learnings were made. For example, modeling was shown to be a useful method to reduce animal experimentation, by enabling in vitro to in vivo correlations or use of simulation to replace experimental methodologies. Mechanism based modeling and simulation was found to be a useful means to translate from preclinical studies to the clinic to ensure progression of the best drug to clinical trials. These models could then be used to optimize design of clinical studies from selection of starting doses to recommended efficacious doses for pivotal trials. Modeling was shown to be beneficial to understand variability in the clinic and to identify factors impacting drug response in individual patients, paving the way for precision medicine strategies, informing clinical diagnostics, biomarkers, and doses for different oncology indications. Pfizer, Inc.; Applied Biomath, LLCPharmacolog

Discussion on the paper “Real-Time Prediction of Clinical Trial Enrollment and Event Counts: A Review”, by DF Heitjan, Z Ge, and GS Ying

No abstract available

Informatics: the fuel for pharmacometric analysis

The current informal practice of pharmacometrics as a combination art and science makes it hard to appreciate the role that informatics can and should play in the future of the discipline and to comprehend the gaps that exist because of its absence. The development of pharmacometric informatics has important implications for expediting decision making and for improving the reliability of decisions made in model-based development. We argue that well-defined informatics for pharmacometrics can lead to much needed improvements in the efficiency, effectiveness, and reliability of the pharmacometrics process. The purpose of this paper is to provide a description of the pervasive yet often poorly appreciated role of informatics in improving the process of data assembly, a critical task in the delivery of pharmacometric analysis results. First, we provide a brief description of the pharmacometric analysis process. Second, we describe the business processes required to create analysis-ready data sets for the pharmacometrician. Third, we describe selected informatic elements required to support the pharmacometrics and data assembly processes. Finally, we offer specific suggestions for performing a systematic analysis of existing challenges as an approach to defi ning the next generation of pharmacometric informatics

Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents: two case studies

Abstract In cancer chemotherapy neutropenia is a common dose-limiting toxicity. An ability to predict the neutropenic effects of cytotoxic agents based on proposed trial designs and models conditioned on previous studies would be valuable. The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials. Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI. Parameter sets were estimated under the following scenarios: (a) data from each trial independently, (b) pooled data from all clinical trials and (c) pooled data from trials performed before the tested trial. Model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks. The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents. For diflomotecan, similar predictions were obtained for the three scenarios. For indisulam predictions were better when based on data from the specific study, however when the model parameters were conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained as when all data were used. This work provides further indication that modeling and simulation tools can be applied in the early stages of drug development to optimize future trials

Using a Conformal Water Bolus to Adjust Heating Patterns of Microwave Waveguide Applicators

Background: Hyperthermia, i.e., raising tissue temperature to 40-45°C for 60 min, has been demonstrated to increase the effectiveness of radiation and chemotherapy for cancer. Although multi-element conformal heat applicators are under development to provide more adjustable heating of contoured anatomy, to date the most often used applicator to heat superficial disease is the simple microwave waveguide. With only a single power input, the operator must be resourceful to adjust heat treatment to accommodate variable size and shape tumors spreading across contoured anatomy. Methods: We used multiphysics simulation software that couples electromagnetic, thermal and fluid dynamics physics to simulate heating patterns in superficial tumors from commercially available microwave waveguide applicators. Temperature distributions were calculated inside homogenous muscle and layered skin-fat-muscle-tumor-bone tissue loads for a typical range of applicator coupling configurations and size of waterbolus. Variable thickness waterbolus was simulated as necessary to accommodate contoured anatomy. Physical models of several treatment configurations were constructed for comparison of simulation results with experimental specific absorption rate (SAR) measurements in homogenous muscle phantom. Results: Accuracy of the simulation model was confirmed with experimental SAR measurements of three unique applicator setups. Simulations demonstrated the ability to generate a wide range of power deposition patterns with commercially available waveguide antennas by controllably varying size and thickness of the waterbolus layer. Conclusion: Heating characteristics of 915 MHz waveguide antennas can be varied over a wide range by controlled adjustment of microwave power, coupling configuration, and waterbolus lateral size and thickness. The uniformity of thermal dose delivered to superficial tumors can be improved by cyclic switching of waterbolus thickness during treatment to proactively shift heat peaks and nulls around under the aperture, thereby reducing patient pain while increasing minimum thermal dose by end of treatment. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE)