Authors:
Prinicipal Scientific Manager: Debasis Pore
Senior Scientific Manager II: Akshay Subramanian
Senior Scientific Manager I: Prashanth Reddy Rikkala, Ashu Srivastav, Veronica Medikare,
Shipra Gupta, Manimaran Asokan
Senior Technical Program Manager I: Harrisham Kaur
Associate Scientist: Tanusree Chaudhuri, Krishna Sameera Devulapalli
Group Head (Biology): Uzma Saeed
Group Head (Computational Biology): Puneet Saxena
Group Head (Program Management): Jitesh Pillai
Vice President (Bio-informatics): Chandra Sekhar Pedamallu
Introduction
Determining the ability of a target to bind to a drug with high affinity to modulate its function with a therapeutic benefit could be termed Druggability. It needs a non-trivial amount of time, financial investment, and resources. An investment of US$2.6 billion (£1.9 billion) over 10-15 years for inventing a new drug from target identification to approval, which ultimately benefits the patients, underscores the importance of prioritizing the most potential druggable targets.
“Effective target identification lies at the core of drug discovery. In theory, it sounds simple — find a target, and match it with a game-changing therapy. In practice, confirming that match is a scientific quest in itself”
Comparable to the high cost, the success rate for drug development programs is substantially low with over 90% of the drug candidates failing in clinical trials, which puts the pharmaceutical industry under mounting pressure. The biggest reason for drug attrition is mainly safety findings or lack of efficacy (Figure 1), nearly 60% of failures are attributed to inappropriate target identification and validation in drug discovery.2,3 To increase the chances of success and maximize return on investment, drug inventors must consider strategies focusing on the identification of the most biologically plausible druggable targets that effectively modulate the disease phenotype on a molecular level.
Selecting the right target early in the discovery R&D process is crucial in enabling the prospects of regulatory approval success. Prioritization of the most promising targets involves consideration of several key factors including disease relevance, role in underlying pathophysiology, specificity to the disease process or state, high frequency in the patient population, tractability (the possibility of finding small molecule compounds with high affinity), safety profile, novelty, as well as the competitiveness in the market. After target identification, the critical imperative is to assess target druggability which is integral to successful target validation and is a pivotal step in every drug design quest. Despite the known 3D protein structures surpassing 100,000 in number and ChEMBL database encompasses around 5,000 known proteins with bindable pockets, a mere 854 of these proteins have been recognized as established therapeutic targets for FDA-approved drugs. These data demonstrate that the eligibility of a protein, and consequently the protein pocket, for binding of a molecule and hence drug discovery is a formidable challenge. Additionally, because a substantial number of small-molecule drug discovery project failures are owing to poor bioavailability, careful evaluation of biological and molecular basis of target druggability and building a precise druggability prediction method (DPM) are extremely essential at the early stage of drug discovery programs.
A drug discovery program starts with the identification of novel and effective drug development technology with biological targets for the development of new drugs with unmet clinical needs. Discovering and evaluating the potential therapeutic benefit of a drug target is founded not only on experimental, mechanistic, and pharmacological studies but also on a theoretical molecular druggability assessment, an early evaluation of potential safety measures, and through considerations regarding opportunities for commercialization as well as options for the generation of IP. Traditional approaches are inadequate for large-scale exploration of novel drug targets, as they are expensive, time-consuming, and laborious. In recent years, various computational strategies for predicting potential druggable proteins have emerged, which commonly use the sequence, structural, and functional features of proteins as input but also system-level properties such as network topological features. Despite tremendous success, unfortunately, many promising and experimentally validated targets are not within the scope of drug modifiability. The Discovery of next-generation technologies including targeting protein degradation, protein stabilizers (RESTORACs), excellent drug delivery systems, targeting PPI, targeting intrinsically disordered regions, as well as targeting protein-DNA binding may provide significant assistance in overcoming these undruggable targets.
Our latest whitepaper "Identifying Druggable Therapeutic Targets: Unveiling promising avenues in Drug Discovery" sheds light on transformative approaches to increase drug discovery and development success rates.
Connect with Excelra experts: marketing@excelra.com
www.excelra.com
