CPT Pharmacometrics & Systems Pharmacology· 2026Q1
Target‐Engagement Prediction From Molecular Structure, In Vitro and Animal Data: A Tutorial for Early Assessment of Clinical Drug Exposure and Therapeutic/Adverse Potential Using the ANDROMEDA and PLATO Software on the InSilicoTrials Platform
- 1citations
- Q1SCImago
- 2026year
Short summary
A new tutorial demonstrates how the ANDROMEDA and PLATO software on the InSilicoTrials platform can predict drug target engagement from molecular structure, in vitro, and animal data, enabling early assessment of clinical exposure and therapeutic/adverse potential.
AI-generated from the title and abstract; the full text is not read.
Key points
- The InSilicoTrials platform's target-engagement predictor can forecast drug responses using molecular structure, in vitro, and animal data.
- ANDROMEDA and PLATO software predict human PK and target affinity from drug SMILES, integrated into PKPD models.
- Predictions from molecular structure were compared against experimental data for carvedilol, ivabradine, and sunitinib.
- User-friendly interfaces facilitate clinical trial simulation for non-programmers.
AI-generated from the title and abstract; the full text is not read.
Abstract
The target-engagement predictor, implemented within the InSilicoTrials cloud-based, AI-powered platform for model-informed drug discovery and development, enables early exploration of time-dependent pharmacological responses. Predictions can be generated using inputs ranging from molecular structure to in vitro, animal, and clinical data. Scaling and translation across species are supported, accounting for differences in body size and plasma protein binding. This tutorial demonstrates the application of the target-engagement predictor for benchmarking and decision-support in the evaluation of new drug candidates. Pharmacokinetic (PK) and pharmacodynamic (PD) data for carvedilol, ivabradine, and sunitinib were used as illustrative examples. The ANDROMEDA and PLATO software were used to predict human clinical PK and target affinity parameters, respectively, based on molecular SMILES representations of the drugs. These predictions were subsequently incorporated into longitudinal PKPD simulation models. User-friendly input wizards and interactive results dashboards were developed to facilitate clinical trial simulation for broader project teams, without requiring programming expertise. Predictions derived from molecular structure were compared with data generated from in vitro, rat, and clinical experimental settings. The target-engagement predictor enables integration of heterogenous data sources to support drug development decisions, including early benefit-risk assessment and dose selection for Phase 1 studies. Direct prediction based on molecular structure alone represents a promising approach for early-stage screening and evaluation of new drug candidates.
The authors' abstract, as published at the source. CPT Pharmacometrics & Systems Pharmacology, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Computational Theory and Mathematics
Computational Theory and MathematicsComputer Science