title: In vitro, in silico and integrated strategies for the estimation of plasma protein binding. A review.
abstract: Plasma protein binding (PPB) strongly affects drug distribution and pharmacokinetic behavior with consequences in overall pharmacological action. Extended plasma protein binding may be associated with drug safety issues and several adverse effects, like low clearance, low brain penetration, drug-drug interactions, loss of efficacy, while influencing the fate of enantiomers and diastereoisomers by stereoselective binding within the body. Therefore in holistic drug design approaches, where ADME(T) properties are considered in parallel with target affinity, considerable efforts are focused in early estimation of PPB mainly in regard to human serum albumin (HSA), which is the most abundant and most important plasma protein. The second critical serum protein α1-acid glycoprotein (AGP), although often underscored, plays also an important and complicated role in clinical therapy and thus the last years it has been studied thoroughly too. In the present review, after an overview of the principles of HSA and AGP binding as well as the structure topology of the proteins, the current trends and perspectives in the field of PPB predictions are presented and discussed considering both HSA and AGP binding. Since however for the latter protein systematic studies have started only the last years, the review focuses mainly to HSA. One part of the review highlights the challenge to develop rapid techniques for HSA and AGP binding simulation and their performance in assessment of PPB. The second part focuses on in silico approaches to predict HSA and AGP binding, analyzing and evaluating structure-based and ligand-based methods, as well as combination of both methods in the aim to exploit the different information and overcome the limitations of each individual approach. Ligand-based methods use the Quantitative Structure-Activity Relationships (QSAR) methodology to establish quantitate models for the prediction of binding constants from molecular descriptors, while they provide only indirect information on binding mechanism. Efforts for the establishment of global models, automated workflows and web-based platforms for PPB predictions are presented and discussed. Structure-based methods relying on the crystal structures of drug-protein complexes provide detailed information on the underlying mechanism but are usually restricted to specific compounds. They are useful to identify the specific binding site while they may be important in investigating drug-drug interactions, related to PPB. Moreover, chemometrics or structure-based modeling may be supported by experimental data a promising integrated alternative strategy for ADME(T) properties optimization. In the case of PPB the use of molecular modeling combined with bioanalytical techniques is frequently used for the investigation of AGP binding.
authors.affiliation: Department of Pharmaceutical Chemistry, School of Pharmacy, University of Athens, Panepistimiopolis, Zografou, Athens 15771, Greece.
authors.forename: George
authors.initials: G
authors.lastname: Lambrinidis
authors.affiliation: Department of Pharmaceutical Chemistry, School of Pharmacy, University of Athens, Panepistimiopolis, Zografou, Athens 15771, Greece.
authors.forename: Theodosia
authors.initials: T
authors.lastname: Vallianatou
authors.affiliation: Department of Pharmaceutical Chemistry, School of Pharmacy, University of Athens, Panepistimiopolis, Zografou, Athens 15771, Greece. Electronic address: tsantili@pharm.uoa.gr.
authors.forename: Anna
authors.initials: A
authors.lastname: Tsantili-Kakoulidou
mesh_terms: D001798:Blood Proteins / Q000378:metabolism*; D003198:Computer Simulation; D006801:Humans; D008024:Ligands; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship
chemical_list: D001798:Blood Proteins; D008024:Ligands; D004364:Pharmaceutical Preparations
keywords: Human serum albumin; In silico predictions; Ligand based modeling; Plasma protein binding; Structure based modeling; α(1)-Acid glycoprotein