Testing Physical Models of Passive Membrane Permeation

Abstract

The biophysical basis of passive membrane permeability is well understood, but most methods for predicting membrane permeability in the context of drug design are based on statistical relationships that indirectly capture the key physical aspects. Here, we investigate molecular mechanics-based models of passive membrane permeability and evaluate their performance against different types of experimental data, including parallel artificial membrane permeability assays (PAMPA), cell-based assays, in vivo measurements, and other in silico predictions. The experimental data sets we use in these tests are diverse, including peptidomimetics, congeneric series, and diverse FDA approved drugs. The physical models are not specifically trained for any of these data sets; rather, input parameters are based on standard molecular mechanics force fields, such as partial charges, and an implicit solvent model. A systematic approach is taken to analyze the contribution from each component in the physics-based permeability model. A primary factor in determining rates of passive membrane permeation is the conformation-dependent free energy of desolvating the molecule, and this measure alone provides good agreement with experimental permeability measurements in many cases. Other factors that improve agreement with experimental data include deionization and estimates of entropy losses of the ligand and the membrane, which lead to size-dependence of the permeation rate.

Optimizing the Permeability and Oral Bioavailability of Macrocycles

“…19,23 Passive permeability is driven primarily by fundamental physical properties that are increasingly well understood and computationally accessible. 40 This holds out the promise of computational predictions capable of guiding the design of orally bioavailable macrocycles, a promise now beginning to be realized. 23,49 …”

“…This simple model provides a way to estimate the permeability coefficient in a more straightforward and less computationally expensive manner. The simpler version of the solubility-diffusion model described in Equation 10.6 has been combined with a conformational sampling algorithm by Jacobson et al 40 Instead of explicitly simulating the permeation process, extensive conformational analyses are performed in (implicit) water and chloroform, which is used as a surrogate for the low dielectric of the membrane. This approximation significantly speeds the calculation, making it practical for comparisons of multiple compounds.…”

“…The free energy of transfer of the membranephilic conformation from water to the membrane appears to be the dominant term in permeation. 43 It is assumed 40,43 that there is only one such conformation in the membrane, whereas there are many in water, based in part on published observations 44 on cyclosporine A. While such approximation helps simplify the computational modeling and produces good correlation with experimental data, 40 exceptions would not be unconceivable.…”

“…23,40,49 The conformational sampling of macrocycles is significantly more challenging than for Rule-of-5 compliant small molecules. The larger size and greater number of rotatable bonds typically observed for macrocycles is one obvious reason.…”

Predicting Efflux Ratios and Blood-Brain Barrier Penetration from Chemical Structure: Combining Passive Permeability with Active Efflux by P-Glycoprotein

“…Similarly, P pass is derived from computed log P m , an estimate of relative passive permeability based on a series of physical factors including polarity, conformational flexibility, and molecular size. 21 Again, we introduce a second fitting coefficient to scale this computed value in an empirical manner:…”