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Factors Affecting Protein-Drug Binding: Drug Interactions01:23

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Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
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Drug binding to proteins is a complex phenomenon influenced by various drug-related factors, each playing a significant role in the interaction between drugs and proteins within the body.
One crucial factor in drug-protein binding is the drug's lipophilicity or its affinity for fat. More lipophilic drugs tend to have higher binding extents. For example, highly lipophilic drugs like cloxacillin exhibit substantial protein binding, with as much as 95% of the drug binding to proteins. In...
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Drug binding to proteins is a key aspect of pharmacokinetics and can influence a drug's distribution, absorption, and elimination in the body. Several factors, including the drug's physiochemical properties, protein concentration, disease states, and the number of binding sites on the protein, influence this process.
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Protein-drug binding, a pivotal aspect of pharmacokinetics, is subject to considerable variability influenced by an array of patient-related factors. The intricate interplay of age, individual differences, and pathological conditions significantly impact the binding dynamics and subsequent pharmacological effects.
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When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug-target

Junjun Zhang1, Minzhu Xie2,3

  • 1Key Laboratory of Computing and Stochastic Mathematics (LCSM) (Ministry of Education), School of Mathematics and Statistics, Hunan Normal University, Changsha, 410081, China.

BMC Bioinformatics
|December 29, 2022
PubMed
Summary

We developed a new computational method, ADA-GRMFC, to accurately predict drug-target interactions (DTIs) faster than existing techniques. This approach improves drug discovery by enhancing the speed and precision of identifying potential drug candidates and their targets.

Keywords:
Drug–target interaction predictionGraph regularized matrix factorizationPrior knowledge consistency constraint

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Area of Science:

  • Computational drug discovery and bioinformatics.
  • Pharmacology and cheminformatics.

Background:

  • Identifying drug-target interactions (DTIs) is crucial for efficient drug development.
  • Traditional experimental methods for DTI identification are time-consuming and costly.
  • Existing computational methods, particularly non-negative matrix factorization (NMF) based approaches, can be improved regarding algorithm convergence and predictive accuracy.

Purpose of the Study:

  • To propose an improved computational method for accurate and rapid prediction of drug-target interactions (DTIs).
  • To address limitations in convergence and performance of existing NMF-based DTI prediction algorithms.

Main Methods:

  • Developed an alternating direction algorithm to solve graph regularized non-negative matrix factorization with prior knowledge consistency constraint (ADA-GRMFC).
  • Constructed DTI, drug similarity, and target similarity matrices using known DTIs, drug chemical structures, and target sequences.
  • Modeled DTI prediction via NMF incorporating graph dual regularization and a prior knowledge consistency constraint to ensure decomposition aligns with known DTIs.

Main Results:

  • ADA-GRMFC demonstrated superior performance compared to state-of-the-art methods in 10-fold cross-validation experiments.
  • The proposed alternating direction algorithm was proven to converge to a stationary point.
  • Case studies showed high accuracy, including correctly predicting the top 10 targets for olanzapine and validating 17 of the top 20 drugs for estrogen receptors alpha.

Conclusions:

  • ADA-GRMFC offers a significant advancement in computational DTI prediction, enhancing accuracy and speed.
  • The method's convergence guarantee and strong validation results support its utility in accelerating drug discovery pipelines.
  • This approach effectively integrates diverse data sources and prior knowledge for robust DTI identification.