The majority (>60%) of clinically important drug targets in humans are either integral or peripheral membrane- bound proteins, including G protein-coupled receptors, ion channels, cytochrome P450 enzymes, and P- glycoprotein. Partitioning of drugs within the lipid cell membrane or membranes of subcellular organelles are known to affect their interactions with such membrane-bound proteins, influencing their efficacy and fate in the body. While membrane interactions can result in better efficacy, selectivity and longer duration of action, the excessive membrane accumulation of drugs causes undesirable toxicities through off-target interactions. Therefore, merely increasing the lipophilicity of drugs to increase potency for membrane-associated targets is often detrimental in terms of the overall drug profile. Thus, the knowledge of quantitative estimate of membrane distribution, preferred location (depth), orientation, and conformation of drug molecule within the bilayer is crucial in understanding its target binding kinetics, onset and duration of drug action, and disposition. Our central hypothesis, formulated based on extensive literature and our preliminary data, is that application of this knowledge to lead optimization in rational drug discovery will result in improved efficacy, selectivity and safety. Hence, the long-term goal is to understand the ?specifics? of the structure-membrane interaction relationship and apply this knowledge to rational design of new therapeutics aimed for membrane-associated targets. To this end, the overall objective of this application is to develop, validate, and apply an integrated in silico approach for fast and accurate prediction of membrane partitioning characteristics, combining all-atom MD simulations and a fragment-based continuum solvent model. The objective of this project will be accomplished by two specific aims: (1) Develop an ?integrated in silico approach? for fast and accurate prediction of percentage distribution, most preferred location, orientation, and conformation of drugs in phospholipid bilayer. The current continuum solvent model will be recalibrated using MD simulation results of 27 structurally diverse chemicals with experimentally known bilayer locations to obtain optimized solvatochromic fragment constants for bilayer distribution prediction. (2) Determine whether the membrane-partitioning characteristics of 17 clinically relevant ?2-adrenergic receptor (?2-AR) agonists and antagonists, quantified by the integrated approach, correlate to their experimental association rates (kon) to the ?2-AR and thus affect their onset and duration of action. This study is innovative because it uses atomistic details of membrane-drug interactions from combined molecular dynamics (MD) simulations and our1 continuum solvent model to understand the structural determinants of receptor-binding kinetics of the studied short- and long-acting anti-asthmatic drugs with diverse structural and physicochemical properties. This proposed study is significant because the expected results will fundamentally advance our knowledge on how specific membrane-drug interactions can be exploited in drug discovery of new therapeutics binding to any membrane-associated receptors, enzymes, and transporters with optimal efficacy and safety.
The majority of clinically important drug targets in humans are either integral or peripheral membrane-bound proteins, and partitioning of drugs within the lipid cell membrane or membranes of subcellular organelles are known to affect their interactions with such membrane-bound targets, influencing their efficacy, safety and fate in the body. The proposed project will develop an integrated in silico approach combining modeling and simulation techniques to accurately quantify this membrane-drug interactions. This study is significant and will have huge impact because the expected results will fundamentally advance our knowledge on how specific membrane-drug interactions can be exploited in rational design of new therapeutics binding to any membrane- associated receptors, enzymes, and transporters with optimal efficacy and safety.