PreClinAI: A Multi-Parametric Simulation Engine for Dynamic Physiological Trajectories
Nabeela Javed · Aug 15, 2026
Abstract. A purely computational pipeline for preclinical drug evaluation would allow toxicity profiles and experimental protocols to be generated directly from molecular structures without premature reliance on live subjects. Standard in-silico models provide part of the solution, but the main ethical and financial benefits are lost if arbitrary in-vivo testing is still required to determine systemic physiological outcomes. We propose a solution to preclinical trial inefficiency using a multi-parametric digital twin framework. The system evaluates molecular features through a calibrated classifier to predict toxicity probabilities, then maps these risks onto simulated biological trajectories modulated by strain genetics, age, and baseline organ health. The resulting physiological simulation allows for precise statistical power analysis, generating the absolute minimum animal sample size required to achieve significance. To ensure interpretability, the architecture grounds its findings by mapping the predicted endpoints against a deterministic biological knowledge graph, forming a mechanistic explanation backed by literature retrieval. The framework requires minimal preliminary in-vivo data, allowing researchers to systematically reduce and refine animal testing models before physical trials commence.