Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D₂ and D₃ Receptors

The binding of a small molecule ligand to its protein target is most often characterized by binding affinity and is typically viewed as an on/off switch. The more complex reality is that binding involves the ligand passing through a series of intermediate states between the solution phase and the fully bound pose. We have performed a set of 29 unbiased molecular dynamics simulations to model the binding pathways of the dopamine receptor antagonists clozapine and haloperidol binding to the D₂ and D₃ dopamine receptors. Through these simulations we have captured the binding pathways of clozapine and haloperidol from the extracellular vestibule to the orthosteric binding site and thereby, we also predict the bound pose of each ligand. These are the first long time scale simulations of haloperidol or clozapine binding to dopamine receptors. From these simulations, we have identified several important stages in the binding pathway, including the involvement of Tyr7.35 in a “handover” mechanism that transfers the ligand between the extracellular vestibule and Asp3.32. We have also performed interaction and cluster analyses to determine differences in binding pathways between the D₂ and D₃ receptors and identified metastable states that may be of use in drug design

Homology Modeling of Human Muscarinic Acetylcholine Receptors

We have developed homology models of the acetylcholine muscarinic receptors M₁R–M₅R, based on the β₂-adrenergic receptor crystal as the template. This is the first report of homology modeling of all five subtypes of acetylcholine muscarinic receptors with binding sites optimized for ligand binding. The models were evaluated for their ability to discriminate between muscarinic antagonists and decoy compounds using virtual screening using enrichment factors, area under the ROC curve (AUC), and an early enrichment measure, LogAUC. The models produce rational binding modes of docked ligands as well as good enrichment capacity when tested against property-matched decoy libraries, which demonstrates their unbiased predictive ability. To test the relative effects of homology model template selection and the binding site optimization procedure, we generated and evaluated a naı̈ve M₂R model, using the M₃R crystal structure as a template. Our results confirm previous findings that binding site optimization using ligand(s) active at a particular receptor, i.e. including functional knowledge into the model building process, has a more pronounced effect on model quality than target–template sequence similarity. The optimized M₁R–M₅R homology models are made available as part of the Supporting Information to allow researchers to use these structures, compare them to their own results, and thus advance the development of better modeling approaches

Computational Models of the Intestinal Environment. 3. The Impact of Cholesterol Content and pH on Mixed Micelle Colloids

In this study, we use molecular dynamics (MD) and experimental techniques (nephelometry and dynamic light scattering) to investigate the influence of cholesterol content and pH on the colloidal structures that form in the gastrointestinal (GI) tract upon lipid digestion. We demonstrate that the ionization state of the molecular species is a primary driver for the self-assembly of aggregates formed by model bile and therefore should be considered when performing <i>in silico</i> modeling of colloidal drug delivery systems. Additionally, the incorporation of physiological concentrations of cholesterol within the model systems does not affect size, number, shape, or dynamics of the aggregates to a significant degree. The MD data shows a reduction in aggregate size with increasing pH, a preference for glycodeoxycholate (GDX) to occupy the aggregate surface, and that the mixed micellar aggregates are oblate spheroids (disc-like). The results obtained assist in understanding the process by which pH and cholesterol influence self-assembly of mixed micelles within the GI tract. The MD approach provides a platform for investigation of interactions of drugs and formulation excipients with the endogenous contents of the GI tract

Computational Models of the Intestinal Environment. 3. The Impact of Cholesterol Content and pH on Mixed Micelle Colloids

In this study, we use molecular dynamics (MD) and experimental techniques (nephelometry and dynamic light scattering) to investigate the influence of cholesterol content and pH on the colloidal structures that form in the gastrointestinal (GI) tract upon lipid digestion. We demonstrate that the ionization state of the molecular species is a primary driver for the self-assembly of aggregates formed by model bile and therefore should be considered when performing <i>in silico</i> modeling of colloidal drug delivery systems. Additionally, the incorporation of physiological concentrations of cholesterol within the model systems does not affect size, number, shape, or dynamics of the aggregates to a significant degree. The MD data shows a reduction in aggregate size with increasing pH, a preference for glycodeoxycholate (GDX) to occupy the aggregate surface, and that the mixed micellar aggregates are oblate spheroids (disc-like). The results obtained assist in understanding the process by which pH and cholesterol influence self-assembly of mixed micelles within the GI tract. The MD approach provides a platform for investigation of interactions of drugs and formulation excipients with the endogenous contents of the GI tract

Computational Models of the Gastrointestinal Environment. 2. Phase Behavior and Drug Solubilization Capacity of a Type I Lipid-Based Drug Formulation after Digestion

Lipid-based drug formulations can greatly enhance the bioavailability of poorly water-soluble drugs. Following the oral administration of formulations containing tri- or diglycerides, the digestive processes occurring within the gastrointestinal (GI) tract hydrolyze the glycerides to mixtures of free fatty acids and monoglycerides that are, in turn, solubilized by bile. The behavior of drugs within the resulting colloidal mixtures is currently not well characterized. This work presents matched in vitro experimental and molecular dynamics (MD) theoretical models of the GI microenvironment containing a digested triglyceride-based (Type I) drug formulation. Both the experimental and theoretical models consist of molecular species representing bile (glycodeoxycholic acid), digested triglyceride (1:2 glyceryl-1-monooleate and oleic acid), and water. We have characterized the phase behavior of the physical system using nephelometry, dynamic light scattering, and polarizing light microscopy and compared these measurements to phase behavior observed in multiple MD simulations. Using this model microenvironment, we have investigated the dissolution of the poorly water-soluble drug danazol experimentally using LC-MS and theoretically by MD simulation. The results show how the formulation lipids alter the environment of the GI tract and improve the solubility of danazol. The MD simulations successfully reproduce the experimental results showing the utility of MD in modeling the fate of drugs after digestion of lipid-based formulations within the intestinal lumen

Computational Models of the Gastrointestinal Environment. 1. The Effect of Digestion on the Phase Behavior of Intestinal Fluids

Improved models of the gastrointestinal environment have great potential to assist the complex process of drug formulation. Molecular dynamics (MD) is a powerful method for investigating phase behavior at a molecular level. In this study we use multiple MD simulations to calculate phase diagrams for bile before and after digestion. In these computational models, undigested bile is represented by mixtures of palmitoyl-oleoylphosphatidylcholine (POPC), sodium glycodeoxycholate (GDX), and water. Digested bile is modeled using a 1:1 mixture of oleic acid and palmitoylphosphatidylcholine (lysophosphatidylcholine, LPC), GDX, and water. The computational phase diagrams of undigested and digested bile are compared, and we describe the typical intermolecular interactions that occur between phospholipids and bile salts. The diffusion coefficients measured from MD simulation are compared to experimental diffusion data measured by DOSY-NMR, where we observe good qualitative agreement. In an additional set of simulations, the effect of different ionization states of oleic acid on micelle formation is investigated

Digestion of Phospholipids after Secretion of Bile into the Duodenum Changes the Phase Behavior of Bile Components

Bile components play a significant role in the absorption of dietary fat, by solubilizing the products of fat digestion. The absorption of poorly water-soluble drugs from the gastrointestinal tract is often enhanced by interaction with the pathways of fat digestion and absorption. These processes can enhance drug absorption. Thus, the phase behavior of bile components and digested lipids is of great interest to pharmaceutical scientists who seek to optimize drug solubilization in the gut lumen. This can be achieved by dosing drugs after food or preferably by formulating the drug in a lipid-based delivery system. Phase diagrams of bile salts, lecithin, and water have been available for many years, but here we investigate the association structures that occur in dilute aqueous solution, in concentrations that are present in the gut lumen. More importantly, we have compared these structures with those that would be expected to be present in the intestine soon after secretion of bile. Phosphatidylcholines are rapidly hydrolyzed by pancreatic enzymes to yield equimolar mixtures of their monoacyl equivalents and fatty acids. We constructed phase diagrams that model the association structures formed by the products of digestion of biliary phospholipids. The micelle–vesicle phase boundary was clearly identifiable by dynamic light scattering and nephelometry. These data indicate that a significantly higher molar ratio of lipid to bile salt is required to cause a transition to lamellar phase (i.e., liposomes in dilute solution). Mixed micelles of digested bile have a higher capacity for solubilization of lipids and fat digestion products and can be expected to have a different capacity to solubilize lipophilic drugs. We suggest that mixtures of lysolecithin, fatty acid, and bile salts are a better model of molecular associations in the gut lumen, and such mixtures could be used to better understand the interaction of drugs with the fat digestion and absorption pathway

Membrane Permeating Macrocycles: Design Guidelines from Machine Learning

The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular “chameleonicity.” Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications

Membrane Permeating Macrocycles: Design Guidelines from Machine Learning

The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular “chameleonicity.” Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications

A Potent Cyclic Peptide Targeting SPSB2 Protein as a Potential Anti-infective Agent

The protein SPSB2 mediates proteosomal degradation of inducible nitric oxide synthase (iNOS). Inhibitors of SPSB2–iNOS interaction may prolong the lifetime of iNOS and thereby enhance the killing of persistent pathogens. We have designed a cyclic peptide, Ac-c­[CVDINNNC]-NH₂, containing the key sequence motif mediating the SPSB2–iNOS interaction, which binds to the iNOS binding site on SPSB2 with a <i>K</i>_d of 4.4 nM, as shown by SPR, [¹H,¹⁵N]-HSQC, and ¹⁹F NMR. An in vitro assay on macrophage cell lysates showed complete inhibition of SPSB2–iNOS interactions by the cyclic peptide. Furthermore, its solution structure closely matched (backbone rmsd 1.21 Å) that of the SPSB2-bound linear DINNN peptide. The designed peptide was resistant to degradation by the proteases pepsin, trypsin, and chymotrypsin and stable in human plasma. This cyclic peptide exemplifies potentially a new class of anti-infective agents that acts on the host innate response, thereby avoiding the development of pathogen resistance