Evaluation of drug–human serum albumin binding interactions with support vector machine aided online automated docking

Abstract

Human serum albumin (HSA), the most abundant plasma protein is well known for its extraordinary binding capacity for both endogenous and exogenous substances, including a wide range of drugs. Interaction with the two principal binding sites of HSA in subdomain IIA (site 1) and in subdomain IIIA (site 2) controls the free, active concentration of a drug, provides a reservoir for a long duration of action and ultimately affects the ADME (absorption, distribution, metabolism, and excretion) profile. Due to the continuous demand to investigate HSA binding properties of novel drugs, drug candidates and drug-like compounds, a support vector machine (SVM) model was developed that efficiently predicts albumin binding. Our SVM model was integrated to a free, web-based prediction platform (http://albumin.althotas.com). Automated molecular docking calculations for prediction of complex geometry are also integrated into the web service. The platform enables the users (i) to predict if albumin binds the query ligand, (ii) to determine the probable ligand binding site (site 1 or site 2), (iii) to select the albumin X-ray structure which is complexed with the most similar ligand and (iv) to calculate complex geometry using molecular docking calculations. Our SVM model and the potential offered by the combined use of in silico calculation methods and experimental binding data is illustrated.

Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools

“…Most of these models are for the prediction of transporter-mediated excretion properties, reflecting the recent efforts for more extensive coverage of key drug efflux and influx transporters and for improved predictive performance by expanded training datasets and appropriate selection of molecular descriptors [8][9][10]. Several models are for the prediction of distribution properties in plasma protein binding and blood brain barrier crossing, reflecting continuous efforts in developing improved predictive models by expanded training datasets and appropriate selection of molecular descriptors [17,62]. 23 The ADME predictive performance of these ML models has typically been measured by the sensitivity SE, specificity SP, and overall accuracy AC, which measure the predictive accuracy for the compounds associated with an ADME property, compounds not associated with the property, and all compounds respectively.…”

“…Therefore, the developed ML regression models are also useful for predicting the activity levels of the ADME and ADME regulatory properties. Other highly evaluated ADME properties covered by the recently and previously [80,91] developed ML models are human intestine absorption, female genital tract penetration [71] in category A and apparent volume of distribution [11], plasma protein binding [17], and blood-brain barrier crossing [62] in category D, and clearance [72] in category E.…”

Ruthenium (II) Complexes Interact with Human Serum Albumin and Induce Apoptosis of Tumor Cells

“…Human serum albumin (HSA) plays a crucial role in drug disposition and pharmacological efficacy [4]. Interaction between HSA and drugs can provide important information bout drug pharmacokinetics [5], such as their storage, transportation, evacuation, etc. Thereupon, the investigations of drugs with respect to small molecules compounds binding have attracted the attention of the biologist, chemist, pharmaceutist, and therapist.…”

Human Serum Albumin-modified Fe3O4 Magnetic Nanoparticles for Affinity-SALDI-MS of Small-Molecule Drugs in Biological Liquids

“…41 The binding of small drugs to HSA occurs via both 2 well-characterized binding on the HSA molecule (i.e., site I and site II) and non-specific binding. [42][43][44][45][46] On the basis of the binding capacity of HSA for various small drugs, an HSA-based high-performance liquid chromatographic (HPLC) column has been developed as a tool to investigate HSA-drug interactions. [47][48][49] Thus, we hypothesized that SALDI-MS using HSA-modified Fe3O4 NPs (HSA-Fe3O4 NPs) would be useful for the extraction of small drugs from complex biological fluids via HSA-small drug interactions.…”