Small-molecule targeting AMPA-mediated excitotoxicity has therapeutic effects in mouse models for multiple sclerosis.

While most research and treatments for multiple sclerosis (MS) focus on autoimmune reactions causing demyelination, it is possible that neurodegeneration precedes the autoimmune response. Hence, glutamate receptor antagonists preventing excitotoxicity showed promise in MS animal models, though blocking glutamate signaling prevents critical neuronal functions. This study reports the discovery of a small molecule that prevents AMPA-mediated excitotoxicity by targeting an allosteric binding site. A machine learning approach was used to screen for small molecules targeting the AMPA receptor GluA2 subunit. The lead candidate has potent effects in restoring neurological function and myelination while reducing the immune response in experimental autoimmune encephalitis and cuprizone MS mouse models without affecting basal neurotransmission or learning and memory. These findings facilitate development of a treatment for MS with a different mechanism of action than current immune modulatory drugs and avoids important off-target effects of glutamate receptor antagonists. This class of MS therapeutics could be useful as an alternative or complementary treatment to existing therapies.

Quetiapine Excretion Into Human Breast Milk.

BACKGROUND: Risk assessment of the use of quetiapine during breastfeeding is challenging owing to a paucity of data. METHODS: A pharmacokinetic study was conducted in lactating women who were taking quetiapine. The primary endpoint was to determine quetiapine concentration profiles in milk and estimated infant exposure levels. Multiple milk and a single blood quetiapine concentrations were determined using a highly sensitive liquid chromatography with tandem mass spectroscopy method. RESULTS: Nine subjects receiving fast-release quetiapine (mean dose, 41 mg/d) were analyzed at steady state. The mean milk/plasma drug concentration ratio at 2-hour postdose was 0.47 (SD, 0.50; range, 0.13-1.67). The mean milk concentration of each patient was 5.7 ng/mL (SD, 4.5; range, 1.4-13.9 ng/mL). The mean infant quetiapine dose via milk per body weight relative to weight-adjusted maternal dose was 0.16 % (SD, 0.08; range, 0.04%-0.35%). CONCLUSIONS: Infant exposure levels to quetiapine via milk are predicted to be very small.

Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization.

Undetected overfitting can occur when there are significant redundancies between training and validation data. We describe AVE, a new measure of training-validation redundancy for ligand-based classification problems, that accounts for the similarity among inactive molecules as well as active ones. We investigated seven widely used benchmarks for virtual screening and classification, and we show that the amount of AVE bias strongly correlates with the performance of ligand-based predictive methods irrespective of the predicted property, chemical fingerprint, similarity measure, or previously applied unbiasing techniques. Therefore, it may be the case that the previously reported performance of most ligand-based methods can be explained by overfitting to benchmarks rather than good prospective accuracy.

Population PK modelling and simulation based on fluoxetine and norfluoxetine concentrations in milk: a milk concentration-based prediction model.

AIMS: Population pharmacokinetic (pop PK) modelling can be used for PK assessment of drugs in breast milk. However, complex mechanistic modelling of a parent and an active metabolite using both blood and milk samples is challenging. We aimed to develop a simple predictive pop PK model for milk concentration-time profiles of a parent and a metabolite, using data on fluoxetine (FX) and its active metabolite, norfluoxetine (NFX), in milk. METHODS: Using a previously published data set of drug concentrations in milk from 25 women treated with FX, a pop PK model predictive of milk concentration-time profiles of FX and NFX was developed. Simulation was performed with the model to generate FX and NFX concentration-time profiles in milk of 1000 mothers. This milk concentration-based pop PK model was compared with the previously validated plasma/milk concentration-based pop PK model of FX. RESULTS: Milk FX and NFX concentration-time profiles were described reasonably well by a one compartment model with a FX-to-NFX conversion coefficient. Median values of the simulated relative infant dose on a weight basis (sRID: weight-adjusted daily doses of FX and NFX through breastmilk to the infant, expressed as a fraction of therapeutic FX daily dose per body weight) were 0.028 for FX and 0.029 for NFX. The FX sRID estimates were consistent with those of the plasma/milk-based pop PK model. CONCLUSIONS: A predictive pop PK model based on only milk concentrations can be developed for simultaneous estimation of milk concentration-time profiles of a parent (FX) and an active metabolite (NFX).

Normalizing molecular docking rankings using virtually generated decoys.

Drug discovery research often relies on the use of virtual screening via molecular docking to identify active hits in compound libraries. An area for improvement among many state-of-the-art docking methods is the accuracy of the scoring functions used to differentiate active from nonactive ligands. Many contemporary scoring functions are influenced by the physical properties of the docked molecule. This bias can cause molecules with certain physical properties to incorrectly score better than others. Since variation in physical properties is inevitable in large screening libraries, it is desirable to account for this bias. In this paper, we present a method of normalizing docking scores using virtually generated decoy sets with matched physical properties. First, our method generates a set of property-matched decoys for every molecule in the screening library. Each library molecule and its decoy set are docked using a state-of-the-art method, producing a set of raw docking scores. Next, the raw docking score of each library molecule is normalized against the scores of its decoys. The normalized score represents the probability that the raw docking score was drawn from the background distribution of nonactive property-matched decoys. Assuming that the distribution of scores of active molecules differs from the nonactive score distribution, we expect that the score of an active compound will have a low probability of having been drawn from the nonactive score distribution. In addition to the use of decoys in normalizing docking scores, we suggest that decoy sets may be a useful tool to evaluate, improve, or develop scoring functions. We show that by analyzing docking scores of library molecules with respect to the docking scores of their virtually generated property-matched decoys, one can gain insight into the advantages, limitations, and reliability of scoring functions.

Virtual decoy sets for molecular docking benchmarks.

Virtual docking algorithms are often evaluated on their ability to separate active ligands from decoy molecules. The current state-of-the-art benchmark, the Directory of Useful Decoys (DUD), minimizes bias by including decoys from a library of synthetically feasible molecules that are physically similar yet chemically dissimilar to the active ligands. We show that by ignoring synthetic feasibility, we can compile a benchmark that is comparable to the DUD and less biased with respect to physical similarity.

A structure-based approach for mapping adverse drug reactions to the perturbation of underlying biological pathways.

Adverse drug reactions (ADR), also known as side-effects, are complex undesired physiologic phenomena observed secondary to the administration of pharmaceuticals. Several phenomena underlie the emergence of each ADR; however, a dominant factor is the drug's ability to modulate one or more biological pathways. Understanding the biological processes behind the occurrence of ADRs would lead to the development of safer and more effective drugs. At present, no method exists to discover these ADR-pathway associations. In this paper we introduce a computational framework for identifying a subset of these associations based on the assumption that drugs capable of modulating the same pathway may induce similar ADRs. Our model exploits multiple information resources. First, we utilize a publicly available dataset pairing drugs with their observed ADRs. Second, we identify putative protein targets for each drug using the protein structure database and in-silico virtual docking. Third, we label each protein target with its known involvement in one or more biological pathways. Finally, the relationships among these information sources are mined using multiple stages of logistic-regression while controlling for over-fitting and multiple-hypothesis testing. As proof-of-concept, we examined a dataset of 506 ADRs, 730 drugs, and 830 human protein targets. Our method yielded 185 ADR-pathway associations of which 45 were selected to undergo a manual literature review. We found 32 associations to be supported by the scientific literature.

Predicting multiple ligand binding modes using self-consistent pharmacophore hypotheses.

The ability to predict ligand binding modes without the aid of wet-lab experiments may accelerate and reduce the cost of drug discovery research. Despite significant recent progress, virtual screening has not yet eliminated the need for wet-lab experiments. For example, after a lead compound has been identified, the precise binding mode is still typically determined by experimental structural biology. This structural knowledge is then employed to guide lead optimization. We present a step toward improving protein-ligand binding mode prediction for a set of ligands known to interact with a common protein. There is thus an important distinction between this work and traditional virtual screening algorithms. Whereas traditional approaches attempt to identify binding ligands from a large database of available compounds, our approach aims to more accurately predict the binding mode for a set of ligands which are already known to bind the target protein. The approach is based on the hypothesis that each active site contains a set of interaction points which binding ligands tend to exploit. In a more traditional context, these interaction points make up a pharmacophoric map. Our algorithm first performs traditional protein-ligand docking for each known binder. The ranked lists of candidate binding modes are then evaluated to identify a set of poses maximally self-consistent with respect to a pharmacophoric map generated from the same poses. We have extensively demonstrated the application of the algorithm to four protein systems (thrombin, cyclin-dependent kinase 2, dihydrofolate reductase, and HIV-1 protease) and attained predictions with an average RMSD < 2.5 A for all tested systems. This represents a typical improvement of 0.5-1.0 A (up to 25%) RMSD over the naive virtual docking predictions. Our algorithm is independent of the docking method and may significantly improve binding mode prediction of virtual docking experiments.

Prediction of sub-cavity binding preferences using an adaptive physicochemical structure representation.

MOTIVATION: The ability to predict binding profiles for an arbitrary protein can significantly improve the areas of drug discovery, lead optimization and protein function prediction. At present, there are no successful algorithms capable of predicting binding profiles for novel proteins. Existing methods typically rely on manually curated templates or entire active site comparison. Consequently, they perform best when analyzing proteins sharing significant structural similarity with known proteins (i.e. proteins resulting from divergent evolution). These methods fall short when used to characterize the binding profile of a novel active site or one for which a template is not available. In contrast to previous approaches, our method characterizes the binding preferences of sub-cavities within the active site by exploiting a large set of known protein-ligand complexes. The uniqueness of our approach lies not only in the consideration of sub-cavities, but also in the more complete structural representation of these sub-cavities, their parametrization and the method by which they are compared. By only requiring local structural similarity, we are able to leverage previously unused structural information and perform binding inference for proteins that do not share significant structural similarity with known systems. RESULTS: Our algorithm demonstrates the ability to accurately cluster similar sub-cavities and to predict binding patterns across a diverse set of protein-ligand complexes. When applied to two high-profile drug targets, our algorithm successfully generates a binding profile that is consistent with known inhibitors. The results suggest that our algorithm should be useful in structure-based drug discovery and lead optimization.

The protein-small-molecule database, a non-redundant structural resource for the analysis of protein-ligand binding.

MOTIVATION: An enabling resource for drug discovery and protein function prediction is a large, accurate and actively maintained collection of protein/small-molecule complex structures. Models of binding are typically constructed from these structural libraries by generalizing the observed interaction patterns. Consequently, the quality of the model is dependent on the quality of the structural library. An ideal library should be non-biased and comprehensive, contain high-resolution structures and be actively maintained. RESULTS: We present a new protein/small-molecule database (the PSMDB) that offers a non-redundant set of holo PDB complexes. The database was designed to allow frequent updates through a fully automated process without manual annotation or filtering. Our method of database construction addresses redundancy at both the protein and the small-molecule level. By efficiently handling structures with covalently bound ligands, we allow our database to include a larger number of structures than previous methods. Multiple versions of the database are available at our web site, including structures of split complexes--the proteins without their binding ligands and the non-covalently bound ligands within their native coordinate frame. AVAILABILITY: http://compbio.cs.toronto.edu/psmdb