In the past, most drugs have been discovered either by identifying the active ingredient from traditional remedies or by serendipitous discovery.  A new approach has been to understand how disease and infection are controlled at the molecular and physiological level and to target specific entities based on this knowledge. The process of drug discovery involves the identification of candidates, synthesis, characterization, screening, and assays for therapeutic efficacy.  Once a compound has shown its value in these tests, it will begin the process of drug development prior to clinical trials.

At the Englander Institute for Precision Medicine (EIPM), we are applying precision medicine to the drug discovery process.  This requires understanding individual patient genetic, health, and lifestyle data to align the best drug or combination of drugs for a particular disease makeup.  We employ computational tools such as disease algorithms to help us understand how a patient will most likely respond to a treatment, so we may identify the treatment that will deliver the best results while minimizing adverse side effects.  Much of the drug screening happens in our lab, using a variety of disease models, instead of a trial and error method using the patient.  This allows us to find the right treatment for the right patient.  We deliver this information to a patient’s clinician so they can make the most informed treatment decisions.


In medicine, biotechnology, and pharmacology, drug discovery is the process by which drugs are discovered and designed.


Drug Discovery Advances

Former EIPM team members, including Drs. Neel S. Madhukar and Kaitlyn Gayvert, under the mentorship of Dr. Olivier Elemento Director of Director of the Englander Institute for Precision Medicine, successfully developed ways towards improving clinical trials and drug development, being part of the 30 under 30 Forbes list for Healthcare.

The video below provides more details on their projects and the impact of their collaboration:

Drug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility.

Over the past decade, the rate of drug attrition due to clinical trial failures has risen substantially. Unfortunately, it is difficult to identify compounds that have unfavorable toxicity properties before conducting clinical trials. Inspired by the effective use of sabermetrics in predicting successful baseball players, we sought to use a similar “Moneyball” approach that analyzes overlooked features to predict clinical toxicity. We introduce a new data-driven approach (PrOCTOR) that directly predicts the likelihood of toxicity in clinical trials. PrOCTOR integrates the properties of a compound’s targets and its structure to provide a new measure, the PrOCTOR score. Drug target network connectivity and expression levels, along with molecular weight, were identified as important indicators of adverse clinical events. Our method provides a data-driven, broadly applicable strategy to identify drugs likely to possess manageable toxicity in clinical trials and will help drive the design of therapeutic agents with less toxicity.