Solutions > Pre-Clinical and Toxicology Data Sciences

Pre-Clinical and Toxicology Data Sciences

Many assays produce complex, interrelated data that is difficult to analyze holistically. High- fidelity Quantitative Systems Pharmacology (QSP) models are growing in size and complexity. In addition, inefficient sample and reagent management between in-house and partner labs can lead to mismatches between what is expected and what is physically available. bPrescient can help with neural network training and modeling/simulation to predict in-vitro and in-vivo assay results, and by providing robust strategies for managing and organizing diverse lab data.

Solution Overview

Our solution for Pre-Clinical and Toxicology Data Sciences includes:

Effective lab data management using ELNs and/or LIMS to organize and link all results for a program or compound, ensuring that physical stock matches expectations.

In-silico prediction of ADME, PK, PD, and toxicity using machine learning and AI, incorporating public models and in-house data.

Flexible enterprise architecture that enables connections between pre-clinical systems and upstream discovery systems for the purposes of refining discovery and screening processes.

PK/PD modeling and mathematical expertise to enable accurate dose estimation for clinical trials.

Solution Benefits

Partnering with bPrescient benefits your R&D program in a variety of ways:

Gaining a comprehensive picture of pre-clinical assay results for lead molecules and programs leads to faster and more accurate decision-making.

Predictive models using in-house and public data enable more confident prioritization of lab assays and validation.

Connections of pre-clinical data with discovery and clinical groups drives more efficient operations across the organization, reducing duplication of effort and delivering cost savings.

Adding QSP capacity to your team as driven by pipeline milestones.

Relevant Expertise

This service draws on our expertise in the following areas:

Strategy & Roadmap Development

Scientific Informatics: Software Design and Implementation

Laboratory Data Systems Design, Integration & Optimization

AI & Machine Learning: Generating Novel Insights

Computational Sciences for Biology & Chemistry Research

Data Engineering, MLOps, and CloudOps

Let’s talk

Tell us about your toughest R&D challenge and let’s discuss how bPrescient can solve it.

Pre-Clinical and Toxicology Data Sciences

Solution Overview

Solution Benefits

Relevant Expertise

Example Tools and Data Sources

Related Case Studies

Preclinical Biostatistics Analysis

Lab Workflow Automation

Lab Informatics Consolidation

Discovery Data Management Roadmap

Let’s talk