Dataset Information

Phenotypic drug discovery: recent successes, lessons learned and new directions.

ABSTRACT: Many drugs, or their antecedents, were discovered through observation of their effects on normal or disease physiology. For the past generation, this phenotypic drug discovery approach has been largely supplanted by the powerful but reductionist approach of modulating specific molecular targets of interest. Nevertheless, modern phenotypic drug discovery, which combines the original concept with modern tools and strategies, has re-emerged over the past decade to systematically pursue drug discovery based on therapeutic effects in realistic disease models. Here, we discuss recent successes with this approach, as well as consider ongoing challenges and approaches to address them. We also explore how innovation in this area may fuel the next generation of successful projects.

SUBMITTER: Vincent F

PROVIDER: S-EPMC9708951 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Phenotypic drug discovery: recent successes, lessons learned and new directions.

Vincent Fabien F Nueda Arsenio A Lee Jonathan J Schenone Monica M Prunotto Marco M Mercola Mark M

Nature reviews. Drug discovery 20220530 12

Many drugs, or their antecedents, were discovered through observation of their effects on normal or disease physiology. For the past generation, this phenotypic drug discovery approach has been largely supplanted by the powerful but reductionist approach of modulating specific molecular targets of interest. Nevertheless, modern phenotypic drug discovery, which combines the original concept with modern tools and strategies, has re-emerged over the past decade to systematically pursue drug discove ...[more]

PMID: 35637317

Similar Datasets

Project description:Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.

Dataset Information

Phenotypic drug discovery: recent successes, lessons learned and new directions.

Publications

Phenotypic drug discovery: recent successes, lessons learned and new directions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets