Peter Wright

Peter joined the Bender research group at the Centre for Molecular Informatics in 2018 through a NC3Rs studentship, following an integrated Master's degree in Chemistry at Imperial College London during which he worked at Pfizer UK Ltd. and spent a year abroad at Universiteit Leiden. His doctoral research involves the following:

• Mining concurrent gene expression and histopathology in-vivo data to elucidate the genetic and pathway level drivers of organ toxicity such as in the kidney through use of algorithms like WGCNA.
• Curating and analysing the proprietary eToxSys database of in-vivo repeat-dose toxicity studies in order to:
  • Retrospectively simulate the impact of using Virtual Control Groups (VCGs) on the statistical detection of treatment-related findings versus using a concurrent control group
  • Further understand the inter-species concordance of histopathological findings and target-organ toxicities between preclinical species used in the current two-species preclinical regulatory toxicity testing paradigm
  • Building in-silico models for secondary in-vivo pharmacokinetic parameters such as C_max and AUC_all, and the dose-linearity of such parameters across large dose-ranges.
• Building predictive models for in-vivo Drug Induced Liver Injury (DILI) based on chemical structure derived descriptors.

The overall aim of Peter's research is to increase understanding of the molecular mechanisms by which compounds can cause adverse events, and ultimately develop improved in-silico and in-vitro screens to detect safety risks to avoid unnecessary investments in preclinical and clinical studies.

Publications in chronological order:

1. Liu, A. et al. Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure. Biology Direct 16, 6 (2021).
2. Wright, P. S. R. et al. Retrospective analysis of the potential use of virtual control groups in preclinical toxicity assessment using the eTOX database. Regulatory Toxicology and Pharmacology 138, 105309 (2023).
3. Wright, P. S. R. et al. Statistical analysis of preclinical inter-species concordance of histopathological findings in the eTOX database. Regulatory Toxicology and Pharmacology 138, 105308 (2023).
4. Zhu, Y. et al. In silico prediction and biological assessment of novel angiogenesis modulators from traditional Chinese medicine. Frontiers in Pharmacology 14, (2023).
5. Handa, K. et al. Prediction of Compound Plasma Concentration–Time Profiles in Mice Using Random Forest. Mol. Pharmaceutics (2023).