Abstract: Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies.

Abstract: Rossmann folds are ancient, frequently diverged domains found in many biological reaction pathways where they have adapted for different functions. Consequently, discernment and classification of their homologous relations and function can be complicated. We define a minimal Rossmann-like structure motif (RLM) that corresponds for the common core of known Rossmann domains and use this motif to identify all RLM domains in the Protein Data Bank (PDB), thus finding they constitute about 20% of all known 3D structures. The Evolutionary Classification of protein structure Domains (ECOD) classifies RLM domains in a number of groups that lack evidence for homology (X-groups), which suggests that they could have evolved independently multiple times. Closely related, homologous RLM enzyme families can diverge to bind different ligands using similar binding sites and to catalyze different reactions. Conversely, non-homologous RLM domains can converge to catalyze the same reactions or to bind the same ligand with alternate binding modes. We discuss a special case of such convergent evolution that is relevant to the polypharmacology paradigm, wherein the same drug (methotrexate) binds to multiple non-homologous RLM drug targets with different topologies. Finally, assigning proteins with RLM domain to the Enzyme Commission classification suggest that RLM enzymes function mainly in metabolism (and comprise 38% of reference metabolic pathways) and are overrepresented in extant pathways that represent ancient biosynthetic routes such as nucleotide metabolism, energy metabolism, and metabolism of amino acids. In fact, RLM enzymes take part in five out of eight enzymatic reactions of the Wood-Ljungdahl metabolic pathway thought to be used by the last universal common ancestor (LUCA). The prevalence of RLM domains in this ancient metabolism might explain their wide distribution among enzymes.

Abstract: PURPOSE: The population of Chile has aged, and in 2017, cancer became the leading cause of death. Since 2005, a national health program has expanded coverage of drugs for 13 types of cancer and related palliative care. We describe the trends in public and private oncology drug expenditures in Chile and consider how increasing expenditures might be addressed. METHODS: We analyzed total quarterly drug expenditures for 131 oncology drugs from quarter (Q)3 2012 until Q1 2017, including public and private insurance payments and patient out-of-pocket spending. The data were analyzed by drug-mix, sources of funding, growth, and intellectual property status. The Laspeyres Price Index was used to analyze expenditure growth. RESULTS: We found 131 oncology drugs associated with 87,129 observations. Spending on drugs rose 120% from the first period, spanning from the first 3 quarters (Q3, Q4, Q1 2012-2013) to the last period (Q3, Q4, Q1 2016-2017), corresponding to an annualized rate of 19.2% and totaling US$398 million (in 2017 dollars). The public sector accounted for 84.2% of spending, which included 50 drugs in the official treatment protocols, whereas private insurance accounted for 7.3% in on-protocol drugs. The remaining 8.5% was paid out of pocket. In the public sector, more than 90% of growth resulted from increased use. Seven drugs, including 3 with nonexpired patents, accounted for 50% of total expenditures. CONCLUSION: Increased use and access enabled by expanded public expenditures drove most of the growth in oncology drug expenditures. However, the rate of public expenditure growth may be fiscally unsustainable. Policies are urgently needed to promote the use of generic drugs, the appropriate mix of on-protocol versus off-protocol drugs, and the curbing of off-label prescribing.

Abstract: Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug-protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.

Abstract: Purpose: The objective of this study was to compare the in vitro Fick's first law, in vitro lipolysis, and in vivo rat assays for oral absorption of Biopharmaceutical Classification Systems Class II (BCS II) drugs in self-nanoemulsifying drug delivery system (SNEDDS), and studied drugs and oils properties effects on the absorption. Methods: The transport abilities of griseofulvin (GRI), phenytoin (PHE), indomethacin (IND), and ketoprofen (KET) in saturated water solutions and SNEDDS were investigated using the in vitro Madin-Darby canine kidney cell model. GRI and cinnarizine (CIN) in medium-chain triglycerides (MCT)-SNEDDS and long-chain triglycerides (LCT)-SNEDDS were administered in the in vivo SD rat and in vitro lipolysis models to compare the oral absorption and the distribution behaviors in GIT and build an in vitro-in vivo correlation (IVIVC). Results: In the cell model, the solubility of GRI, PHE, IND, and KET increased 6-8 fold by SNEDDS, but their permeability were only 18%, 4%, 8%, and 33% of those of their saturated water solutions, respectively. However, in vivo absorption of GRI-SNEDDS was twice that of the GRI suspension and those of CIN-SNEDDS were 15-21 fold those of the CIN suspension. In the lipolysis model, the GRI% in aqueous and pellet phases of MCT were similar to that in LCT. In contrast, the CIN% in the aqueous and pellet phases were decreased but that of the lipid phase increased. In addition, an IVIVC was found between the CIN% in the lipid phase and in vivo relative oral bioavailability (F r). Conclusion: The in vitro cell model was still a suitable tool to study drug properties effects on biofilm transport and SNEDDS absorption mechanisms. The in vitro lipolysis model provided superior oral absorption simulation of SNEDDS and helped to build correlation with in vivo rats. The oral drug absorption was affected by drug and oil properties in SNEDDS.

Abstract: Drug discovery is an academical and commercial process of global importance. Accurate identification of drug-target interactions (DTIs) can significantly facilitate the drug discovery process. Compared to the costly, labor-intensive and time-consuming experimental methods, machine learning (ML) plays an ever-increasingly important role in effective, efficient and high-throughput identification of DTIs. However, upstream feature extraction methods require tremendous human resources and expert insights, which limits the application of ML approaches. Inspired by the unsupervised representation learning methods like Word2vec, we here proposed SPVec, a novel way to automatically represent raw data such as SMILES strings and protein sequences into continuous, information-rich and lower-dimensional vectors, so as to avoid the sparseness and bit collisions from the cumbersomely manually extracted features. Visualization of SPVec nicely illustrated that the similar compounds or proteins occupy similar vector space, which indicated that SPVec not only encodes compound substructures or protein sequences efficiently, but also implicitly reveals some important biophysical and biochemical patterns. Compared with manually-designed features like MACCS fingerprints and amino acid composition (AAC), SPVec showed better performance with several state-of-art machine learning classifiers such as Gradient Boosting Decision Tree, Random Forest and Deep Neural Network on BindingDB. The performance and robustness of SPVec were also confirmed on independent test sets obtained from DrugBank database. Also, based on the whole DrugBank dataset, we predicted the possibilities of all unlabeled DTIs, where two of the top five predicted novel DTIs were supported by external evidences. These results indicated that SPVec can provide an effective and efficient way to discover reliable DTIs, which would be beneficial for drug reprofiling.

Abstract: Paracoccidioidomycosis (PCM) is the most prevalent endemic mycosis in Latin America. The disease is caused by fungi of the genus Paracoccidioides and mainly affects low-income rural workers after inhalation of fungal conidia suspended in the air. The current arsenal of chemotherapeutic agents requires long-term administration protocols. In addition, chemotherapy is related to a significantly increased frequency of disease relapse, high toxicity, and incomplete elimination of the fungus. Due to the limitations of current anti-PCM drugs, we developed a computational drug repurposing-chemogenomics approach to identify approved drugs or drug candidates in clinical trials with anti-PCM activity. In contrast to the one-drug-one-target paradigm, our chemogenomics approach attempts to predict interactions between drugs, and Paracoccidioides protein targets. To achieve this goal, we designed a workflow with the following steps: (a) compilation and preparation of Paracoccidioides spp. genome data; (b) identification of orthologous proteins among the isolates; (c) identification of homologous proteins in publicly available drug-target databases; (d) selection of Paracoccidioides essential targets using validated genes from Saccharomyces cerevisiae; (e) homology modeling and molecular docking studies; and (f) experimental validation of selected candidates. We prioritized 14 compounds. Two antineoplastic drug candidates (vistusertib and BGT-226) predicted to be inhibitors of phosphatidylinositol 3-kinase TOR2 showed antifungal activity at low micromolar concentrations (<10 muM). Four antifungal azole drugs (bifonazole, luliconazole, butoconazole, and sertaconazole) showed antifungal activity at low nanomolar concentrations, validating our methodology. The results suggest our strategy for predicting new anti-PCM drugs is useful. Finally, we could recommend hit-to-lead optimization studies to improve potency and selectivity, as well as pharmaceutical formulations to improve oral bioavailability of the antifungal azoles identified.

Abstract: A subtractive genomic approach has been utilized for the identification of potential drug targets and vaccine candidates in Clostridium botulinum, the causative agent of flaccid paralysis in humans. The emergence of drug-resistant pathogenic strains has become a significant global public health threat. Treatment with antitoxin can target the neurotoxin at the extracellular level, however, can't converse the paralysis caused by botulism. Therefore, identification of drug targets and vaccine candidates in C. botulinum would be crucial to overcome drug resistance to existing antibiotic therapy. A total of 1729 crucial proteins, including chokepoint, virulence, plasmid and resistance proteins were mined and used for subtractive channel of analysis. This analysis disclosed 15 potential targets, which were non-similar to human, gut micro flora, and anti-targets in the host. The cellular localization of 6 targets was observed in the cytoplasm and might be used as a drug target, whereas 9 targets were localized in extracellular and membrane bound proteins and can be used as vaccine candidates. Furthermore, 4 targets were observed to be homologous to more than 75 pathogens and hence are considered as broad-spectrum antibiotic targets. The identified drug and vaccine targets in this study would be useful in the design and discovery of novel therapeutic compounds against botulism.

Abstract: BACKGROUND: In the literature-based discovery, considerable research has been done based on the ABC model developed by Swanson. ABC model hypothesizes that there is a meaningful relation between entity A extracted from document set 1 and entity C extracted from document set 2 through B entities that appear commonly in both document sets. The results of ABC model are relations among entity A, B, and C, which is referred as paths. A path allows for hypothesizing the relationship between entity A and entity C, or helps discover entity B as a new evidence for the relationship between entity A and entity C. The co-occurrence based approach of ABC model is a well-known approach to automatic hypothesis generation by creating various paths. However, the co-occurrence based ABC model has a limitation, in that biological context is not considered. It focuses only on matching of B entity which commonly appears in relation between two entities. Therefore, the paths extracted by the co-occurrence based ABC model tend to include a lot of irrelevant paths, meaning that expert verification is essential. METHODS: In order to overcome this limitation of the co-occurrence based ABC model, we propose a context-based approach to connecting one entity relation to another, modifying the ABC model using biological contexts. In this study, we defined four biological context elements: cell, drug, disease, and organism. Based on these biological context, we propose two extended ABC models: a context-based ABC model and a context-assignment-based ABC model. In order to measure the performance of the both proposed models, we examined the relevance of the B entities between the well-known relations "APOE-MAPT" as well as "FUS-TARDBP". Each relation means interaction between neurodegenerative disease associated with proteins. The interaction between APOE and MAPT is known to play a crucial role in Alzheimer's disease as APOE affects tau-mediated neurodegeneration. It has been shown that mutation in FUS and TARDBP are associated with amyotrophic lateral sclerosis(ALS), a motor neuron disease by leading to neuronal cell death. Using these two relations, we compared both of proposed models to co-occurrence based ABC model. RESULTS: The precision of B entities by co-occurrence based ABC model was 27.1% for "APOE-MAPT" and 22.1% for "FUS-TARDBP", respectively. In context-based ABC model, precision of extracted B entities was 71.4% for "APOE-MAPT", and 77.9% for "FUS-TARDBP". Context-assignment based ABC model achieved 89% and 97.5% precision for the two relations, respectively. Both proposed models achieved a higher precision than co-occurrence-based ABC model.

Abstract: The classical form of alpha1-antitrypsin deficiency (ATD) is characterized by intracellular accumulation of the misfolded variant alpha1-antitrypsin Z (ATZ) and severe liver disease in some of the affected individuals. In this study, we investigated the possibility of discovering novel therapeutic agents that would reduce ATZ accumulation by interrogating a C. elegans model of ATD with high-content genome-wide RNAi screening and computational systems pharmacology strategies. The RNAi screening was utilized to identify genes that modify the intracellular accumulation of ATZ and a novel computational pipeline was developed to make high confidence predictions on repurposable drugs. This approach identified glibenclamide (GLB), a sulfonylurea drug that has been used broadly in clinical medicine as an oral hypoglycemic agent. Here we show that GLB promotes autophagic degradation of misfolded ATZ in mammalian cell line models of ATD. Furthermore, an analog of GLB reduces hepatic ATZ accumulation and hepatic fibrosis in a mouse model in vivo without affecting blood glucose or insulin levels. These results provide support for a drug discovery strategy using simple organisms as human disease models combined with genetic and computational screening methods. They also show that GLB and/or at least one of its analogs can be immediately tested to arrest the progression of human ATD liver disease.