Patient-derived model systems and the development of next-generation anticancer therapeutics

Curr Opin Chem Biol. 2020 Jun;56:72-78. doi: 10.1016/j.cbpa.2020.01.002. Epub 2020 Feb 18.

Abstract

Anticancer drug discovery and development using conventional cell line and animal models has traditionally had a low overall success rate. Despite yielding game-changing new therapeutics, 10-20 new molecules have to be brought to the clinic to obtain one new approval, making this approach costly and inefficient. The use of in vitro experimental models based on primary human tumour tissues has the potential to provide a representation of human cancer biology that is closer to an actual patient and to 'bridge the translational gap' between preclinical and clinical research. Here, we review recent advances in the use of human tumour samples for preclinical research through organoid development or as primary patient materials. While challenges still remain regarding analysis, validation and scalability, evidence is mounting for the applicability of both models as preclinical research tools.

Keywords: Drug discovery; High-content screening; Machine learning; Organoids; Primary cancer models; Primary patient samples; Screening; Single-cell analysis; Single-cell imaging; Translational medicine.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • Antineoplastic Agents / pharmacology*
  • Drug Evaluation, Preclinical
  • Drug Screening Assays, Antitumor
  • High-Throughput Screening Assays
  • Humans
  • Machine Learning
  • Models, Biological
  • Neoplasms / diagnostic imaging*
  • Neoplasms / therapy*
  • Optical Imaging
  • Organoids / metabolism
  • Single-Cell Analysis

Substances

  • Antineoplastic Agents