Anesthetic Drug Discovery with Computer-Aided Drug Design 

In the 19th century, William T.G. Morton transformed the field of medicine by pioneering the use of ether anesthesia in surgical procedures. Since then, anesthesia has become an indispensable component of the surgical operating room, valued for its ability to provide analgesia, sedation, immobility, hypnosis, amnesia, and autonomic suppression. Despite their widespread use, current anesthetics can cause adverse reactions including respiratory depression, hypotension, nausea, vomiting, or adrenal suppression. Developing new drugs is a costly venture, in terms of time, financial expense, and human resources, with a high failure rate. The advancement of artificial intelligence and computer-aided methods has significantly facilitated modern anesthetic drug discovery. Since its emergence in the 1960s, computer-aided drug design (CADD) has proven instrumental in reducing costs, saving time, filtering drugs, and overall facilitating the drug development process.1

CADD techniques can be categorized into two groups: structure-based and ligand-based drug design. Structure-based drug design identifies target proteins and obtains their 3D structures, using them to predict how potential drugs will bind and behave. A target’s structure may be obtained through methods like homology modeling, molecular docking,2 high-throughput screening, and molecular dynamics. Ligand-based drug design focuses on analyzing active compounds to identify structural or physicochemical features that account for biological activity.3 Methods include QSAR modeling, similarity search, pharmacophore modeling, and clinical demand-oriented reverse drug design.

The anesthetic drug discovery process begins with target elucidation. This process can be divided into target identification (which identifies biological targets, such as proteins, enzymes, or receptors) and target validation (which aims to understand target–ligand interactions). An example of CADD techniques in target elucidation comes from a 2021 computational study on opioids. Opioid receptor Kappa 1 (OPRK1), an opioid receptor subtype, is widely used to activate pain-inhibitory pathways. Researchers combined several techniques into a computer-aided prediction of how different molecules might fit with the OPRK1 receptor to identify potential anesthetic drugs that would act on this pathway.4 In another study, researchers employed molecular dynamics simulations to identify new conformational states of the opioid μ receptor and used those results to find molecules that could bind to it more effectively.5 With molecular dynamics, they were able to observe how the receptor changes over time, and they used unsupervised machine learning methods to virtually test-dock different receptor–compound combinations. When they applied their trained model to real molecular libraries, they found several new unknown compounds that exhibited strong binding affinity for the opioid μ receptor.6

Another key component of the drug discovery process is hit and lead discovery, which refers to the stages of drug development where promising compounds are identified and then optimized to improve their activity, selectivity, and overall drug-like properties. In an in silico study, researchers discovered prospective anesthetic agents using virtual screening of 50,000 compounds. Based on drug likeness, absorption, distribution, metabolism, excretion, and toxicity, they identified 5 compounds that demonstrated strong binding affinity to the GABAA protein, a common target of known anesthetic agents.7

The evolution of computer-aided drug design has revolutionized the search for safer and more effective anesthetic drugs. By integrating structure-based and ligand-based design methods with advanced computational simulations, researchers can model complex receptor behaviors, predict drug–target interactions, and identify promising compounds, all before beginning the costly process of clinical trials. Studies on opioid and GABA receptors highlight how these approaches can accelerate drug discovery while deepening our understanding of anesthetic mechanisms at the molecular level. As artificial intelligence and molecular modeling continue to advance, CADD holds immense potential to continue transforming the field of anesthesiology.

References

  1. Liu X., Xue Z., Luo M., Ke B., Jiancheng L., Anesthetic Drug Discovery With Computer-Aided Drug Design and Machine Learning. Anesthesiology and Perioperative Science. 2024;2(1). https://doi.org/10.1007/s44254-023-00047-x
  2. Meng X.Y., Zhang H.X., Mezei M., Cui M., Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery. Current Computer Aided-Drug Design. 2011;7(2):146-157. https://doi.org/10.2174/157340911795677602
  3. Macalino S.J.Y., Gosu V., Hong S., Choi S., Role of Computer-Aided Drug Design in Modern Drug Discovery. Archives of Pharmacal Research. 2015;38(9):1686-1701. https://doi.org/10.1007/s12272-015-0640-5
  4. Sripriya A.V., Menon V., Baudry J., Whittle J., Novel Big Data-Driven Machine Learning Models for Drug Discovery Application. Molecules. 2022;27(3):594. https://doi.org/10.3390/molecules27030594
  5. Feinberg E.N., Barati F.A., Uprety R., et al. Machine Learning Harnesses Molecular Dynamics to Discover New μ Opioid Chemotypes.  2018. https://doi.org/10.48550/arxiv.1803.04479
  6. Feinberg E.N., Barati F.A., Uprety R., et al. Machine Learning Harnesses Molecular Dynamics to Discover New μ Opioid Chemotypes. arXiv. 2018, https://arxiv.org/abs/1803.04479
  7. Peng Q.X., Guan X.H., Yi Z.G., Su Y.P., In-silico Approaches in Anesthetic Drug Development: Computer Aided Drug Designing. Drug Research. 2014;65(11):587-591. https://doi.org/10.1055/s-0034-1395564