"In silico" prediction of blood-brain barrier permeation and P-glycoprotein activity

P-glycoprotein is an ATP-dependent efflux transport protein which is highly expressed in many human tissues such as the intestinal epithelium and the blood-brain barrier, and is over-expressed in many cancer cells.1 This transporter carries a wide variety of chemically unrelated compounds. It binds them within the cell lipid membrane, and flips them to the outer leaflet or exports them to the extracellular medium.2 Since P-glycoprotein affects the distribution of many drugs, assessing the interactions between drugs and Pglycoprotein at an early stage of drug development is important. It has been shown that the binding of a drug to the transporter occurs in a two-step process.3{5 (i) The drug partitions from the extracellular environment to the lipid membrane, and after diffusion to the inner cytosolic leaflet of the bilayer, (ii) it binds to P-glycoprotein most likely via hydrogen bond formation. Different methods have been used to assess the lipid-water partition coefficient, such as isothermal titration calorimetry, and lipid monolayer insertion measurements. However, the lipid-water partition coefficient depends on the lipid used, and in turn on the lateral packing density of the lipid layer. Therefore an approach based on surface activity measurements was developed, which allows the prediction of the lipid-water partition coe�cient for membranes of different lateral packing densities.7 Measurements of the surface pressure of the drug in buffer solution as a function of concentration (Gibbs adsorption isotherm) yields the air-water partition coefficient (Kaw), the critical micellar concentration (CMC), and the cross-sectional area of the compound (AD), provided experiments are performed under conditions of minimal electrostatic repulsion. Since air has a dielectric constant close to that of the lipid core region of a membrane, there is a direct relationship between the partition of a drug into the air-water interface, and the partition into the lipid-water interface.8 The cross-sectional area, as well as the lipid-water partition coefficient (and by extension the air-water partition coefficient), are thus crucial parameters to assess the binding and diffusion of a drug into a lipid bilayer. In a first part of the thesis, I focused on the membrane binding step. Since the cross-sectional area of a compound is a crucial parameter for drug partitioning into the lipid bilayer, the quality of the data obtained by mean of surface activity measurements are most important. For this purpose, in a first step, I improved the calibration of the experimental settings, by assessing several factors like the evaporation or the solvent effect. In a second step, I developed computer routines for unbiased evaluation of these measurements. In a third step, I developed an algorithm to calculate the cross-sectional area of a compound oriented at a hydrophilic-hydrophobic interface; this algorithm has been calibrated on a set of measured data, in order to find from a conformational ensemble the conformation of the membrane-bound drug. In a second part of the thesis, I focused on the binding of a drug to P-glycoprotein. P-glycoprotein is monitored essentially by three types of assays, (i) the measurement of ATP hydrolysis activity of the transporter, (ii) a competition assay against calcein-AM, and (iii) a transcellular transport assay through polarized P-glycoprotein over-expressing cell monolayer. Based on a modular binding approach to assess the two-step binding of a drug to P-glycoprotein (Figure 1),5 I developed several rules to predict the outcome of these experimental assays. Each rule, predicting one particular assay, has been tested on experimental datasets. In a third part of the thesis, I developed a working interface to handle multiple structures of compounds, to calculate the new descriptors involved in the two-step binding of drugs to P-glycoprotein (membrane partitioning, and binding to the transporter), and to calculate the outcome of the prediction rules. Moreover the working interface has been designed in a way the user can easily define new rules, or even introduce a new multidrug transporter (e.g. the multidrug transporter MRP1). Starting from well characterized physical-chemical parameters, I developed a coherent ensemble of descriptors to assess by a rule-based approach the thermodynamics and kinetics of P-glycoprotein activation. This ensemble has been embedded in a customizable working interface, allowing easy evaluation of the in silico predictions

DeepCt: Predicting pharmacokinetic concentration-time curves and compartmental models from chemical structure using deep learning

After initial triaging using in vitro absorption, distribution, metabolism, and excretion (ADME) assays, pharmacokinetic (PK) studies are the first application of promising drug candidates in living mammals. Pre-clinical PK studies characterize the evolution of the compound’s concentration over time, typically in rodents’ blood or plasma. From this concentration-time (C-t) profiles, PK parameters such as total exposure or maximum concentration can be subsequently derived. An early estimation of compounds’ PK offers the promise of reducing animal studies and cycle times by selecting and designing molecules with increased chances of success at the PK stage. Even though C-t curves are the major readout from a PK study, most machine learning-based prediction efforts have focused on the derived PK parameters instead of C-t profiles, likely due to the lack of approaches to model the underlying ADME mechanisms. Herein, a novel deep learning approach termed DeepCt is proposed for the prediction of C-t curves from the compound structure. Our methodology is based on the prediction of an underlying mechanistic compartmental PK model, which enables further simulations, and predictions of single- and multiple-dose C-t profiles