The initial phase of drug discovery and development involves the identification of a target, which is a specific entity that undergoes a change in its behavior or function when bound by an endogenous ligand or a drug candidate, serving as a critical step to understanding the mechanism of action for chosen pharmaceutical compounds¹. Target validation is the process that confirms whether targeting a specific entity offers potential therapeutic advantages².

If the target fails the validation, it won't progress further in the drug development pipeline². Insufficient validation of drug targets in the early stages of development has been linked to costly clinical trial failures and a lower rate of drug approvals. More effective target validation and early proof-of-concept studies could substantially reduce phase II clinical trial failures, consequently lowering the cost of developing new molecular entities, underscoring the critical role of robust target validation in drug discovery.³

Understanding target identification

Target identification entails pinpointing the specific molecular target, such as a protein or nucleic acid, with which a small molecule interacts. In clinical pharmacology, this process is focused on identifying the primary target responsible for the effectiveness of a drug, pharmaceutical compound, or other foreign substance⁴.

Target discovery utilizing genetic or genomic approaches takes advantage of the convenience of manipulating DNA and RNA for extensive modifications and measurements, often employing the concept of genetic interaction, where genetic modifiers (enhancers or suppressors) are used to generate hypotheses about potential targets⁵.

Proteomic analysis is a method for discovering drug targets by comparing the variations in cellular proteins before and after drug intervention, allowing the identification of elements that specifically impact protein expression, offering a more comprehensive approach compared to Transcriptomics⁶.

High throughput screening (HTS) involves the rapid testing of thousands to millions of samples for biological activity at various levels using automated equipment and has become the standard method in the drug discovery process for understanding interactions between molecules of interest and biological systems⁷.

Challenges lie in the time and resource-intensive character of specific target assessments and the limited interpretability and replicability of outcomes generated by machine learning methods, constraining their utility in the process of target identification⁸. Moreover, the detection of targets with low abundance, weak binding, or membrane proteins presents difficulties in the target identification process⁹. Validation of hits and the deconvolution of targets are also substantial challenges in the realm of phenotypic drug discovery¹⁰.

Importance of target validation in drug discovery

Target validation is the process of subjecting a potential drug target to a series of rigorous experiments and investigations, confirming its direct involvement in a specific biological pathway, and demonstrating its capacity to produce a therapeutic effect, thus establishing its suitability for further drug development¹¹.

After identifying appropriate target candidates, the process of target validation is undertaken to confirm the involvement of a target in the relevant biological process and assess the potential benefits of modulating it.

Target validation involves initial computer modeling to screen targets for potential drug interactions, followed by in vivo or in vitro validation techniques that include methods like gene knockouts, RNA interference, antisense technology, and similar approaches, followed by the analysis of resulting phenotypes such as cellular fitness and proliferation¹².

Strategies for drug target validation

A widely employed method is the cell-based assay, which entails the cultivation of cells in a controlled setting and examining how they react to particular drug compounds. This approach enables scientists to gauge the effects of the drug on cellular functions and its suitability as a potential therapeutic candidate. As an illustration, the Cellular Thermal Shift Assay (CETSA) is one such cell-based assay that quantifies the interaction of drugs with specific proteins inside cells¹³. Quantitative polymerase chain reaction (qPCR) is a broadly applied technique for examining the expression profiles of specific genes and can offer crucial insights into how drug treatments affect gene expression levels¹⁴.

Mouse models are considered a highly reliable system for confirming potential targets. Tumor cell line xenograft models are commonly utilized for in vivo validation of these targets due to their manageability, adaptability, and ability to mimic the genetic variations found in human tumors¹⁵.

In vivo methods offer the advantage of confirming that a compound can interact with and impact the cellular target within a living organism¹⁶. Nonetheless, the validation of drug targets in live organisms can pose difficulties and consume a substantial amount of time. This process necessitates the employment of animal models, which might not consistently mirror human physiology and disease states¹⁷. In vitro techniques offer cost-effective, quicker, ethically sound, and less labor-intensive alternatives for assessing drug absorption. A significant drawback of in vitro drug validation is the challenge of applying the findings to real clinical scenarios¹⁸. An illustration of this is the lymphocyte transformation test, frequently employed in diagnosing drug hypersensitivity, which faces limitations in effectively translating T cell proliferation observed in vitro to the clinical context¹⁹.

Conclusion

Application of chemical proteomics, enables the identification of protein targets at the proteomic level²⁰. This method entails creating chemical probes that can specifically bind to desired proteins, followed by retrieving and identifying these proteins. Progress in mass spectrometry techniques has further played a crucial role in the process of biological target identification and validation²¹.

Artificial intelligence (AI) represents a sophisticated approach for identifying new targets and uncovering innovative drugs within biological networks. This is possible because these networks can robustly maintain and quantitatively assess the interactions between various components of cell systems associated with human diseases like cancer²². Machine learning methods enhance decision-making within the pharmaceutical field, improving the analysis of data in various applications such as QSAR analysis, identifying promising compounds, and creating new drug structures, all of which contribute to obtaining precise and reliable results²³.

Pharmacophore models enable target identification for active drug molecules, aiding in understanding drug mechanisms and exploring drug repositioning and polypharmacology. Activity-based protein profiling (ABPP) with mass spectrometry is effective for proteome-wide target identification, particularly in the case of ATP-binding proteins, revealing a wide array of potential drug targets²⁴.

References

1. Wang S, Sim TB, Kim YS, Chang YT. Tools for target identification and validation. Current Opinion in Chemical Biology. 2004 ;8(4):371–7.
2. Pankevich DE, Altevogt BM, Dunlop J, Gage FH, Hyman SE. Improving and Accelerating Drug Development for Nervous System Disorders. Neuron. 2014;84(3):546–53.
3. Emmerich CH, Gamboa LM, Hofmann MCJ, Bonin-Andresen M, Arbach O, Schendel P, et al. Improving target assessment in biomedical research: the GOT-IT recommendations. Nat Rev Drug Discov. 2021;20(1):64–81.
4. Tabana Y, Babu D, Fahlman RP, Siraki AG, Barakat K. Target identification of small molecules: an overview of the current applications in drug discovery. BMC Biotechnol. 2023;23(1).
5. Schenone M, Dančík V, Wagner BK, Clemons PA. Target identification and mechanism of action in chemical biology and drug discovery. Nat Chem Biol. 2013;9(4):232–40.
6. Li G, Peng X, Guo Y, Gong S, Cao S, Qiu F. Currently Available Strategies for Target Identification of Bioactive Natural Products. Front Chem. 2021;9:761609.
7. Mayr LM, Bojanic D. Novel trends in high-throughput screening. Curr Opin Pharmacol. 2009;9(5):580–8.
8. Emmerich CH, Gamboa LM, Hofmann MCJ, Bonin-Andresen M, Arbach O, Schendel P, et al. Improving target assessment in biomedical research: the GOT-IT recommendations. Nat Rev Drug Discov. 2021;20(1):64–81.
9. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18(6):463–77.
10. Moffat JG, Vincent F, Lee JA, Eder J, Prunotto M. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat Rev Drug Discov. 2017;16(8):531–43.
11. Pankevich DE, Altevogt BM, Dunlop J, Gage FH, Hyman SE. Improving and accelerating drug development for nervous system disorders. Neuron. 2014;84(3):546–53.
12. Emmerich CH, Gamboa LM, Hofmann MCJ, Bonin-Andresen M, Arbach O, Schendel P, et al. Improving target assessment in biomedical research: the GOT-IT recommendations. Nature Reviews Drug Discovery. 2021;20(1):64–81.
13. Molina DM, Jafari R, Ignatushchenko M, Seki T, Larsson EA, Dan C, et al. Monitoring Drug Target Engagement in Cells and Tissues Using the Cellular Thermal Shift Assay. Science. 2013;341(6141):84–7.
14. Lu K, Li T, He J, Chang W, Zhang R, Liu M, et al. qPrimerDB: a thermodynamics-based gene-specific qPCR primer database for 147 organisms. Nucleic Acids Research. 2017;46(D1):D1229–36.
15. Wang S, Sim TB, Kim YS, Chang YT. Tools for target identification and validation. Current Opinion in Chemical Biology. 2004;8(4):371–7.
16. La Rosa V, Poce G, Canseco JO, Buroni S, Pasca MR, Biava M, et al. MmpL3 Is the Cellular Target of the Antitubercular Pyrrole Derivative BM212. Antimicrobial Agents and Chemotherapy. 2012;56(1):324–31.
17. Thakker DR, Natt F, Dieter Hüsken, Maier RM, Müller MM, Herman, et al. Neurochemical and behavioral consequences of widespread gene knockdown in the adult mouse brain by using nonviral RNA interference. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(49):17270–5.
18. Xu Y, Shrestha N, Préat V, Beloqui A. An overview of in vitro, ex vivo and in vivo models for studying the transport of drugs across intestinal barriers. Advanced Drug Delivery Reviews. 2021;175:113795.
19. Pichler WJ, Tilch J. The lymphocyte transformation test in the diagnosis of drug hypersensitivity. Allergy. 2004 [cited 2022];59(8):809–20.
20. Ha J, Park H, Park J, Park SB. Recent advances in identifying protein targets in drug discovery. Cell Chemical Biology. 2021;28(3):394–423.
21. Chen X, Wang Y, Ma N, Tian J, Shao Y, Zhu B, et al. Target identification of natural medicine with chemical proteomics approach: probe synthesis, target fishing and protein identification. Signal Transduction and Targeted Therapy. 2020;5(1).
22. You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduction and Targeted Therapy. 2022;7(1).
23. Dara S, Dhamercherla S, Jadav SS, Babu CM, Ahsan MJ. Machine Learning in Drug Discovery: A Review. Artificial Intelligence Review. 2021;55(3).
24. Chen X, Wong YK, Wang J, Zhang J, Lee YM, Shen HM, et al. Target identification with quantitative activity based protein profiling (ABPP). PROTEOMICS. 2016;17(3-4):1600212.

recent-articles