Predictive screening model for potential vector-mediated transport of cationic substrates at the blood–brain barrier choline transporter

A set of semi-rigid cyclic and acyclic bis-quaternary ammonium analogs, which were part of a drug discovery program aimed at identifying antagonists at neuronal nicotinic acetylcholine receptors, were investigated to determine structural requirements for affinity at the blood–brain barrier choline transporter (BBB CHT). This transporter may have utility as a drug delivery vector for cationic molecules to access the central nervous system. In the current study, a virtual screening model was developed to aid in rational drug design/ADME of cationic nicotinic antagonists as BBB CHT ligands. Four 3D-QSAR comparative molecular field analysis (CoMFA) models were built which could predict the BBB CHT affinity for a test set with an r² <0.5 and cross-validated q² of 0.60, suggesting good predictive capability for these models. These models will allow the rapid in silico screening of binding affinity at the BBB CHT of both known nicotinic receptor antagonists and virtual compound libraries with the goal of informing the design of brain bioavailable quaternary ammonium analogs that are high affinity selective nicotinic receptor antagonists.     

QSAR-based solubility model for drug-like compounds

Solubility plays a very important role in the selection of compounds for drug screening. In this context, a QSAR model was developed for predicting water solubility of drug-like compounds. First, a set of relevant parameters for establishing a drug-like chemical space was defined. The comparison of chemical structures from the FDAMDD and PHYSPROP databases allowed the selection of properties that were more efficient in discriminating drug-like compounds from other chemicals. These filters were later on applied to the PHYSPROP database and 1174 chemicals fulfilling these criteria and with experimental solubility information available at 25 °C were retained. Several QSAR solubility models were developed from this set of compounds, and the best one was selected based on the accuracy of correct classifications obtained for randomly chosen training and validation subsets. Further validation of the model was performed with a set of 102 drugs for which experimental solubility data have been recently reported. A good agreement between the predictions and the experimental values confirmed the reliability of the QSAR model.     

Commentary: Why Pharmaceutical Scientists in Early Drug Discovery Are Critical for Influencing the Design and Selection of Optimal Drug Candidates

This commentary reflects the collective view of pharmaceutical scientists from four different organizations with extensive experience in the field of drug discovery support. Herein, engaging discussion is presented on the current and future approaches for the selection of the most optimal and developable drug candidates. Over the past two decades, developability assessment programs have been implemented with the intention of improving physicochemical and metabolic properties. However, the complexity of both new drug targets and non-traditional drug candidates provides continuing challenges for developing formulations for optimal drug delivery. The need for more enabled technologies to deliver drug candidates has necessitated an even more active role for pharmaceutical scientists to influence many key molecular parameters during compound optimization and selection. This enhanced role begins at the early in vitro screening stages, where key learnings regarding the interplay of molecular structure and pharmaceutical property relationships can be derived. Performance of the drug candidates in formulations intended to support key in vivo studies provides important information on chemotype-formulation compatibility relationships. Structure modifications to support the selection of the solid form are also important to consider, and predictive in silico models are being rapidly developed in this area. Ultimately, the role of pharmaceutical scientists in drug discovery now extends beyond rapid solubility screening, early form assessment, and data delivery. This multidisciplinary role has evolved to include the practice of proactively taking part in the molecular design to better align solid form and formulation requirements to enhance developability potential.     

Recent Advances in Physicochemical and ADMET Profiling in Drug Discovery

The drastic increase in the cost for discovering and developing a new drug along with the high attrition rate of development candidates led to shifting drug‐discovery strategy to parallel assessment of comprehensive drug physicochemical, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties alongside efficacy. With the proposal of a profiling paradigm and utilization of integrated risk assessment, one can exponentially enhance the predictive power of in vitro tools by taking into consideration the interplay among profiling parameters. In particular, this article will review recent advances in accurate assessment of solubility and other physicochemical parameters. The proper interpretation of these experimental data is crucial for rapid and meaningful risk assessment and rational optimization of drug candidates in drug discovery. The impact of these tools on assisting drug‐discovery teams in establishing in vitro–in vivo correlation (IVIVC) as well as structure–property relationship (SPR) will be presented.     

Stabilizing Additives Added during Cell Lysis Aid in the Solubilization of Recombinant Proteins

Insoluble recombinant proteins are a major issue for both structural genomics and enzymology research. Greater than 30% of recombinant proteins expressed in Escherichia coli (E. coli) appear to be insoluble. The prevailing view is that insolubly expressed proteins cannot be easily solubilized, and are usually sequestered into inclusion bodies. However, we hypothesize that small molecules added during the cell lysis stage can yield soluble protein from insoluble protein previously screened without additives or ligands. We present a novel screening method that utilized 144 additive conditions to increase the solubility of recombinant proteins expressed in E. coli. These selected additives are natural ligands, detergents, salts, buffers, and chemicals that have been shown to increase the stability of proteins in vivo. We present the methods used for this additive solubility screen and detailed results for 41 potential drug target recombinant proteins from infectious organisms. Increased solubility was observed for 80% of the recombinant proteins during the primary and secondary screening of lysis with the additives; that is 33 of 41 target proteins had increased solubility compared with no additive controls. Eleven additives (trehalose, glycine betaine, mannitol, L-Arginine, potassium citrate, CuCl₂, proline, xylitol, NDSB 201, CTAB and K₂PO₄) solubilized more than one of the 41 proteins; these additives can be easily screened to increase protein solubility. Large-scale purifications were attempted for 15 of the proteins using the additives identified and eight (40%) were prepared for crystallization trials during the first purification attempt. Thus, this protocol allowed us to recover about a third of seemingly insoluble proteins for crystallography and structure determination. If recombinant proteins are required in smaller quantities or less purity, the final success rate may be even higher.     

The use of transgenic systems in pharmaceutical research.

Those pharmaceutical companies whose goal is to generate novel innovative drugs are faced with the challenge that only a fraction of the compounds tested in clinical trials eventually become a registered drug. This problem of attrition is compounded by the fact that the clinical trial or development stage is by far the most costly phase of bringing a new drug to market, consuming around 80 per cent of the total spend. Transgenic technology represents an attractive approach to reducing the attrition rate of compounds entering clinical trials by increasing the quality of the target and compound combinations making the transition from discovery into development. Transgenic technology can impact at many points in the discovery process, including target identification and target validation, and provides models designed to alert researchers early to potential problems with drug metabolism and toxicity, as well as providing better models for human diseases. In target identification, transgenic animals harbouring large DNA fragments can be used to narrow down genetic regions. Genetic studies often result in the identification of large genomic regions and one way to decrease the region size is to do complementation studies in transgenic animals using, for example, inserts from bacterial artificial chromosome (BAC) clones. In target validation, transgenic animals can be used for in vivo validation of a specific target. Considerable efforts are being made to establish new, rapid and robust tools with general utility for in vivo validation, but, so far, only transgenic animals work reliably on a wide range of targets. Transgenic animals can also be used to generate better disease models. Predictive animal models to test new compounds and targets will significantly speed up the drug discovery process and, more importantly, increase the quality of the compounds taken further in the research and development process. Humanised transgenic animals harbouring the human target molecule can be used to understand the effect of a compound acting on the human target in vivo. Also, models mimicking human drug metabolism will provide a means of assessing the effect of human-specific metabolites and of understanding the pharmacokinetic properties of potential drugs. In toxicology studies, transgenic animals are providing more predictive models. A good example of this are those models routinely used to look for carcinogenicity associated with new compounds.     

The Open Access Malaria Box: A Drug Discovery Catalyst for Neglected Diseases

Historically, one of the key problems in neglected disease drug discovery has been identifying new and interesting chemotypes. Phenotypic screening of the malaria parasite, Plasmodium falciparum has yielded almost 30,000 submicromolar hits in recent years. To make this collection more accessible, a collection of 400 chemotypes has been assembled, termed the Malaria Box. Half of these compounds were selected based on their drug-like properties and the others as molecular probes. These can now be requested as a pharmacological test set by malaria biologists, but importantly by groups working on related parasites, as part of a program to make both data and compounds readily available. In this paper, the analysis and selection methodology and characteristics of the compounds are described.     

化合物ADMET性质预测平台的构建

在药物研发早期阶段对化合物成药性和安全性进行评估,对于提高药物研发成功率、降低研发成本具有十分重要的意义。为了能够帮助药物研究工作者快速准确地判断候选化合物的成药性与安全性,开发了一个基于计算机方法的化合物ADMET性质预测平台。首先,通过文本挖掘的方法收集了化合物药代动力学性质和毒性(ADMET)的高质量实验数据。然后,根据原始文献复原了13个预测模型,同时采用支持向量机方法自建了15个具有较高预测能力的计算模型。最后,基于分布式架构,结合高性能计算集群优势,开发了化合物ADMET性质预测平台(http://www.vslead.com/?r=admet/index),用于预测28种重要的化合物ADMET性质。研究者可以使用这一平台快捷方便地对药物研究中比较重要的ADMET性质进行预测,在药物研发早期对候选化合物进行成药性评价和风险评估,有助于提高药物研的成功率,节省研发时间和经费的投入。     

Identification and Characterization of Hundreds of Potent and Selective Inhibitors of Trypanosoma brucei Growth from a Kinase-Targeted Library Screening Campaign

In the interest of identification of new kinase-targeting chemotypes for target and pathway analysis and drug discovery in Trypanosomal brucei, a high-throughput screen of 42,444 focused inhibitors from the GlaxoSmithKline screening collection was performed against parasite cell cultures and counter-screened against human hepatocarcinoma (HepG2) cells. In this way, we have identified 797 sub-micromolar inhibitors of T. brucei growth that are at least 100-fold selective over HepG2 cells. Importantly, 242 of these hit compounds acted rapidly in inhibiting cellular growth, 137 showed rapid cidality. A variety of in silico and in vitro physicochemical and drug metabolism properties were assessed, and human kinase selectivity data were obtained, and, based on these data, we prioritized three compounds for pharmacokinetic assessment and demonstrated parasitological cure of a murine bloodstream infection of T. brucei rhodesiense with one of these compounds (NEU-1053). This work represents a successful implementation of a unique industrial-academic collaboration model aimed at identification of high quality inhibitors that will provide the parasitology community with chemical matter that can be utilized to develop kinase-targeting tool compounds. Furthermore these results are expected to provide rich starting points for discovery of kinase-targeting tool compounds for T. brucei, and new HAT therapeutics discovery programs.     

Integrating computer-based de novo drug design and multidimensional filtering for desirable drugs

In the pharmaceutical industry today, many of the potent compounds discovered using expensive technologies are eventually rejected because of poor physicochemical or absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) properties. This problem can be addressed by placing fast and accurate computational technologies at the heart of drug discovery. Chemically diverse and potent compounds generated by de novo design algorithms are scored for ADME/Tox properties using rigorously validated statistical models. Every molecule passing through this in silico pipeline is thus associated with a wealth of predicted properties, thereby allowing for rapid assessment to determine which molecule should be further developed. Critical to this idea is a platform that allows for the efficient exchange of in silico and experimental data between all scientists regardless of specialization. By bridging the gap between the in silico and experimental cultures in this fashion, an information-driven, cost-effective drug discovery program can be realized.     

Deep understanding of structure-solubility relationship for a diverse set of organic compounds using matched molecular pairs

Aqueous solubility is an important biopharmaceutical property in drug discovery and development. Although it has been studied for decades, the impact on solubility by the substructures (or fragments) of compounds are still not fully understood and characterized. This study aims to obtain fragment-solubility relationships using matched molecular pairs, and to provide further insight and suggestions for chemists on structural modifications to improve solubility profiles of drug-like molecules. A set of 2794 compounds with measured intrinsic aqueous solubility (logS) was fragmented into rings, linkers, and R groups using a controlled hierarchical fragmentation method. Then matched molecular pairs that differ by only one chemical transformation (i.e., addition or substitution of fragments) were identified and analyzed. The difference in solubility for each matched molecular pair was calculated, and the impact of the corresponding chemical transformation on solubility was investigated. The final product of this study was a fragment-solubility knowledgebase containing relative contributions to solubility of various medicinal chemistry design elements (R-groups, linkers, and rings). Structural modifications that might improve solubility profiles, that is, addition/deletion/substitution of fragments, could be derived from this knowledgebase. This knowledgebase could be used as an expert tool in lead optimization to improve solubility profiles of compounds, and the analysis method could be applied to study other biological and ADMET properties of organic compounds.     

Melanization of Cryptococcus neoformans in Murine Infection

Cryptococcus neoformans is a fungus that is pathogenic in humans and that can produce melanin in vitro. Melanization is associated with virulence, but there is no evidence that melanin is made during infection. Melanins are difficult to study because they are amorphous and insoluble. Melanin-binding peptides from a phage display library were used to demonstrate that C. neoformans makes melanin-like compounds in tissue. Melanin-binding peptides were characterized by a high proportion of positively charged and aromatic residues. Two other methods, demonstration of an antibody response to melanin in mice infected with C. neoformans and analysis of yeast cell walls in infected tissue by light microscopy, were used to support these findings. The demonstration that C. neoformans melanizes in tissue has important implications for pathogenesis and drug discovery.     

Biopartitioning micellar chromatography: an in vitro technique for predicting human drug absorption

The main oral drug absorption barriers are fluid cell membranes and generally drugs are absorbed by a passive diffusion mechanism. Biopartitioning micellar chromatography (BMC) is a mode of micellar liquid chromatography that uses micellar mobile phases of Brij35 under adequate experimental conditions and can be useful to mimic the drug partitioning process in biological systems. In this paper the usefulness of BMC for predicting oral drug absorption in humans is demonstrated. A hyperbolic model has been obtained using the retention data of a heterogeneous set of 74 compounds, which shows predictive ability for drugs absorbed by passive diffusion. The model obtained in BMC is compared with those obtained using the well-known systems (Caco-2 and TC-7) that use intestinal epithelium cell lines. The use of BMC is simple, reproducible and can provide key information about the transport properties of new compounds during the drug discovery process.     

Preparation and Evaluation of Solid Dispersions of A New Antitumor Compound Based on Early-Stage Preparation Discovery Concept

Ensuring sufficient drug solubility is a crucial problem in pharmaceutical-related research. For water-insoluble drugs, various formulation approaches are employed to enhance the solubility and bioavailability of lead compounds. The goal of this study was to enhance the dissolution and absorption of a new antitumor lead compound, T-OA. Early-stage preparation discovery concept was employed in this study. Based on this concept, a solid dispersion system was chosen as the method of improving drug solubility and bioavailability. Solid dispersions of T-OA in polyvinylpyrrolidone (PVP) K30 were prepared by the solvent evaporation method. Dissolution testing determined that the ideal drug-to-PVP ratio was 1:5. X-ray diffraction, Fourier transform infrared spectroscopy, and differential scanning calorimetry were employed to confirm the formation of solid dispersions. Scanning electron microscopy demonstrated that T-OA was converted into an amorphous form. Both in vitro dissolution testing and the in vivo studies demonstrated that the solubility and bioavailability of T-OA were significantly improved when formulated in a solid dispersion with PVP. The dissolution rate of the T-OA/PVP solid dispersion was greatly enhanced relative to the pure drug, and the relative bioavailability of T-OA solid dispersions was found to be 392.0%, which is 4-fold higher than the pure drug.     

Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described.     

High-throughput ligand screening via preclustering and evolved neural networks

The pathway for novel lead drug discovery has many major deficiencies, the most significant of which is the immense size of small molecule diversity space. Methods that increase the search efficiency and/or reduce the size of the search space, increase the rate at which useful lead compounds are identified. Artificial neural networks optimized via evolutionary computation provide a cost and time-effective solution to this problem. Here, we present results that suggest preclustering of small molecules prior to neural network optimization is useful for generating models of quantitative structure-activity relationships for a set of HIV inhibitors. Using these methods, it is possible to prescreen compounds to separate active from inactive compounds or even actives and mildly active compounds from inactive compounds with high predictive accuracy while simultaneously reducing the feature space. It is also possible to identify "human interpretable" features from the best models that can be used for proposal and synthesis of new compounds in order to optimize potency and specificity.