Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.

Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature-based interpretability method, which can be seamlessly integrated into supervised ML models to gain a deeper understanding of their predictions, thereby enhancing their transparency and trustworthiness. This tutorial focuses on the application of SHAP analysis to standard ML black-box models for regression and classification problems.

Integrating machine learning with pharmacokinetic models: Benefits of scientific machine learning in adding neural networks components to existing PK models.

Recently, the use of machine-learning (ML) models for pharmacokinetic (PK) modeling has grown significantly. Although most of the current approaches use ML techniques as black boxes, there are only a few that have proposed interpretable architectures which integrate mechanistic knowledge. In this work, we use as the test case a one-compartment PK model using a scientific machine learning (SciML) framework and consider learning an unknown absorption using neural networks, while simultaneously estimating other parameters of drug distribution and elimination.

Upadacitinib Population Pharmacokinetics and Exposure-Response Relationships in Ulcerative Colitis Patients.

Background And Objective: Upadacitinib, an oral selective and reversible Janus kinase (JAK) inhibitor, showed favorable efficacy and safety in patients with moderate-to-severe ulcerative colitis (UC). The objective was to characterize upadacitinib pharmacokinetics in UC patients across Phase 2b and 3 trials and evaluate the relationships between upadacitinib plasma exposures and key efficacy or safety endpoints.

Methods: Population pharmacokinetics and exposure-response analyses were performed to characterize upadacitinib pharmacokinetics in UC patients and evaluate the relationships between plasma exposures and key efficacy or safety endpoints at the end of 8-week induction and 52-week maintenance periods.

Age Structure Can Account for Delayed Logistic Proliferation of Scratch Assays.

Scratch assays are in vitro methods for studying cell migration. In these experiments, a scratch is made on a cell monolayer and recolonisation of the scratched region is imaged to quantify cell migration rates. Typically, scratch assays are modelled by reaction diffusion equations depicting cell migration by Fickian diffusion and proliferation by a logistic term.

In vitro cell migration quantification method for scratch assays.

The scratch assay is an in vitro technique used to assess the contribution of molecular and cellular mechanisms to cell migration. The assay can also be used to evaluate therapeutic compounds before clinical use. Current quantification methods of scratch assays deal poorly with irregular cell-free areas and crooked leading edges which are features typically present in the experimental data.

A stochastic model for tumour control probability that accounts for repair from sublethal damage.

The tumour control probability (TCP) is the probability that a treatment regimen of radiation therapy (RT) eradicates all tumour cells in a given tissue. To decrease the toxic effects on healthy cells, RT is usually delivered over a period of weeks in a series of fractions. This allows tumour cells to repair sublethal damage (RSD) caused by radiation.