Target-based drug discovery strategies involve understanding the disease mechanism of action, followed by target identification and validation, hit identification, hit-to-lead, and lead optimization. Significant strides have been made in identifying novel therapeutic interventions, however, failure rates are high, resulting in lost efforts, resource depletion, and financial risk/loss. Innovative strategies are needed to shorten the research and development cycle, increase process efficiencies, and accelerate the drug discovery process.
This article discusses some strategies that can be used to develop new therapeutic candidates that are not achievable with current approaches.
PROTACS for drug discovery
Current targeted drug discovery approaches can be used to target only 20-25% of known protein targets such as kinases or G protein-coupled receptors (GPCRs). The remaining 75–80% of “non-drug” target proteins may lack catalytic activity or possess catalytically independent functions. In some cases, the protein targets may have multiple functions and catalytic domains, and blocking just one of the catalytic sites may not be sufficient to elicit an effective response or may result in incomplete effectiveness.
PROteolysis Targeting Chimera (PROTAC), a chemical knockdown approach that degrades the target protein, is a novel drug discovery strategy that may overcome the challenges associated with current approaches. dr Clara Recasens Zorzo, postdoc at the Institut de Génétique Moléculaire de Montpellier (IGMM), Univ. Montpellier, CNRS, explains: “The classic strategy for the development of new targeted therapies was to synthesize a molecule to target the function a target protein. The drug therefore had to bind to a very specific functional region of the protein of interest in order to be active (e.g. the catalytic site of an enzyme). PROTAC technology changes this drug discovery paradigm because its mechanism of action is designed to be remove the target protein from the cell.”
PROTACs are heterobifunctional small molecule degraders that include:
1. A ligand that binds to a target protein
2. A ligand that binds to E3 ubiquitin ligase
3. A linker to conjugate these two ligands
PROTACs hijack the ubiquitin proteasome system (UPS) for protein destruction. A PROTAC acts as a chemical bridge, bringing a target protein close to an active E3 ligase complex to form a ternary complex. Formation of the ternary complex is the first step in the cascade of events leading to ubiquitination and subsequent degradation of the target protein using UPS. The target protein is degraded by the 26S proteasome, which is part of the UPS. Elimination of the “non-drug” target protein results in loss of target protein function.
PROTACs offer several advantages over traditional drug discovery strategies. “The PROTAC simply has to specifically bind somewhere in the target to induce its degradation. This new strategy offers several advantages: Many proteins involved in disease physiopathology, which were considered untreatable because they do not have an easily targeted catalytic site (ie, transcription factors), have now become potential PROTAC targets. In addition, cell specificity can be achieved by selecting the E3 ligase that will recruit the PROTAC. For example, we know that cancer cells overexpress specific E3 ligases compared to healthy tissue, and coupling PROTAC to this specific E3 ligand will confer specificity on the cancer cell and reduce possible side effects,” says Recasens Zorzo.
In addition, various drug modalities may benefit from PROTACs. Recasens Zorzo says, “Drugs other than small molecules, such as the emerging peptide drugs, can be converted into PROTACs, allowing this technology to bypass the limitations of small molecules.”
Another attractive feature is that PROTACs can be recycled and used for subsequent rounds of mining. “PROTAC’s mode of action is to catalyze the breakdown of its target protein, a single PROTAC molecule can be recycled and is able to break down multiple target molecules. This can reduce the IC50 [half maximal inhibitory concentration] of the drug along with toxicity and cost,” explains Recasens Zorzo.
ARV-110 and ARV-471 are the first PROTACs to show encouraging results in cancer studies. ARV-110 targets the androgen receptor for the potential treatment of men with metastatic castration-resistant prostate cancer (mCRPC) who have made advances on existing therapies. ARV-471 is an investigational PROTAC that specifically targets and depletes the estrogen receptor for the treatment of patients with locally advanced or metastatic ER+/HER2 breast cancer. Efforts to design PROTACs targeting the major protease (Mprofessional) run from SARS-CoV-2, a key protein required for viral replication. To do this, computational approaches such as protein-protein docking were used to evaluate the potential complementarity between a cereblon E3 ligase and Mprofessional of SARS-CoV-2 and possible linker length.
While beneficial, developing a PROTAC is easier said than done. There are several issues related to the bioavailability and metabolic stability of PROTACs. In addition, each of the components of a PROTAC can influence its effectiveness and must be carefully optimized. Further research can certainly address many of the challenges, and the use of computational approaches can provide important insights into these systems. Computational approaches to accelerating drug discovery are discussed in more detail in the rest of the article.
Computer methods for target prediction
Identifying important macromolecular targets is an important step in the development of new drugs. However, target identification with experimental approaches is a complex, lengthy and costly affair with an uncertain outcome.
Computational methods for target prediction in drug discovery can complement experimental methods and have received significant attention from researchers worldwide. dr Johannes Kirchmair, Associate Professor of Cheminformatics, working at the Institute of Pharmaceutical Sciences, Department of Pharmaceutical Chemistry at the University of Vienna and head of the Computational Drug Discovery and Design Group, says: “Modern computer-aided methods such as machine learning (ML) can make a significant contribution to target identification Afford. They are increasingly able to identify the structural patterns crucial for the binding of compounds to specific proteins and other biomacromolecules. Part of the value of computational methods is that they can immediately assess whether or not a reliable prediction of the target(s) of a compound of interest is possible. In other words, they can provide immediate feedback based on the available knowledge about the specific chemical space of interest and thus also about the novelty of a compound of interest.”
Computational methods can be applied to almost every step in the drug research and development process. dr Simone Brogi, Assistant Professor at the Institute of Pharmacy at the University of Pisa says: “Computer-assisted processes can positively influence the entire drug development process. Through a more targeted search, computer-based methods increase the hit rate of new potential therapeutic molecules with significant cost and time savings compared to classic high-throughput screening and combinatorial chemistry techniques. The goal of in silico Drug discovery approaches consist not only of explaining the chemical basis of the therapeutic effect, but also of identifying potential derivatives that would enhance activity (hit-to-lead optimization).
Several computational methods capable of proposing putative targets with good success rates have been developed. Brogi explains: “If the three-dimensional structure of the target is known, it can be applied with the help of a computer Techniques mainly focused on molecular docking using various algorithms (e.g. incremental construction, genetic algorithm, Monte Carlo) and scoring functions (e.g. physics-based or force field-based, empirical, knowledge-based and ML-based scoring functions) around three-dimensional Build complexes of a specific target and potential ligands and study a chosen system at the atomic level.”
The binding affinity of the ligands for a given target can also be estimated using molecular mechanical methods such as MMGB(PB)/SA. Brogi explains: “Typically, the stability of selected complexes is assessed using molecular dynamics simulation approaches, where it is possible to study the evolution of a given system in explicit solvent for a selected time and also to study the ligand-protein interaction (currently being microseconds and seconds of simulation due to the large increase in computing power to routine experiments). By coupling molecular docking and molecular dynamics, it is possible to develop computational protocols to identify putative drug candidates for a specific target involved in a specific disorder.”
The dramatic increase in the availability of information for both biological macromolecules and small molecules is one of the main drivers of advances in computational drug discovery. Brogi explains the advantages of computational methods in drug discovery: “[They] have the advantage of accelerating the production and screening of potential novel therapeutics based on the analysis of computed properties and predictive models for selected drug targets, as well as the identification of safety risks, while reducing the need for costly and time-consuming studies.”
Computational approaches have great potential and are intended to complement existing experimental methods.
“Computational methods do not aim to replace experiments, but to guide experimenters so that they can focus their resources on the most promising research directions and thus advance drug discovery,” concludes Kirchmair.