Polypharmacology is the concept of designing or identifying drugs that interact with multiple biological targets rather than a single specific target. This approach recognizes the complexity of biological systems and leverages multi-target interactions to achieve enhanced efficacy, reduce resistance, or modulate complex disease pathways, especially in multifactorial diseases such as cancer, CNS disorders, and infectious diseases. In polypharmacology, it’s wise to consider the similarity of binding sites e.g. if a binding site benefits from an ionic interaction, a second target that does not benefit from such an interaction might hamper the design of ligands that engage both sites. Another consideration might be pocket size. Instead of targeting multiple sites with a single drug, polypharmacy (targeting multiple site with multiple drugs) offers an alternative strategy. However, this requires multiple optimization campaigns including for dosing, pharmacokinetics and toxicity.
Importance in Computational Drug Discovery:
- Enables the rational design of drugs with improved efficacy by modulating multiple targets within a disease network. In turn, lower doses per target might be required to achieve therapeutic efficacy, reducing off-target toxicity.
- Reduces the likelihood of drug resistance by simultaneously inhibiting redundant or compensatory pathways.
- Supports drug repurposing by identifying off-target effects that may be therapeutically beneficial.
- Facilitates the prediction and mitigation of adverse effects arising from unintended polypharmacological interactions.
- Drives the development of network pharmacology and systems biology approaches for holistic drug discovery.
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