Combinatorial Chemistry & High Throughput Screening - Volume 18, Issue 3, 2015

Due to the time and effort requirements for the development of a new drug, and the high attrition rates associated with this developmental process, there is an intense effort by academic and industrial researchers to find novel ways for more effective drug development schemes. The first step in the discovery process of a new drug is the identification of the lead compound. The modern research tendency is to avoid the synthesis of new molecules based on chemical intuition, which is time and cost consuming, and instead to apply in silico rational drug design. This approach reduces the consumables and human personnel involved in the initial steps of the drug design. In this review real examples from our research activity aiming to discover new leads will be given for various dire warnings diseases. There is no recipe to follow for discovering new leads. The strategy to be followed depends on the knowledge of the studied system and the experience of the researchers. The described examples constitute successful and unsuccessful efforts and reflect the reality which medicinal chemists have to face in drug design and development. The drug stability is also discussed in both organic molecules and metallotherapeutics. This is an important issue in drug discovery as drug metabolism in the body can lead to various toxic and undesired molecules.

Malaria has continued to be one of the most perplexing diseases for biological science community around the world due to its prevalent devastating nature and quick developing resistance against the frontline drugs. Artimisinin-based combination therapy (ACT) has been so far found to be among the best therapies against Plasmodium pathogens but alarming emergence of resistance in parasites against every known chemotherapy has prompted the scientific community to step up all the efforts towards development of new and affordable anti-malarial drugs. Computer-aided approaches have received enormous attention in recent years in the field of identification and design of novel drugs. In this review, we summarize recently published research concerning the identification and development of anti-malarial compounds using virtual screening approaches. It would be admirable to discern the successful application of in silico studies for anti-malarial drug discovery hitherto and would certainly help in generating new avenues for pursuing integrated studies between the experimentalists and computational chemists in a systematic manner as a time and cost efficient alternative for future antimalarial drug discovery projects.

The hERG potassium channel is currently emerging as a potential target for the treatment of some forms of arrhythmias or to contrast an unintentional channel block caused by drugs. Despite its therapeutic relevance, so far only few compounds are described as able to enhance channel function by potentiating hERG currents. This gap is also related to the lack of hERG crystal structure which strongly limits the possibility to employ structure-based techniques in the search and design of novel activators. To overcome this limitation, in the present work, a ligand-based virtual screening was performed using as separate search queries two conformations of NS1643, the most deeply investigated and better characterized hERG activator. The library of compounds resulting from the virtual screening was then clustered based on recurring chemical features, and 5 hits were selected to be evaluated for their ability to enhance hERG current in vitro. Compound 3 showed a good activating effect, also displaying a mechanism of action similar to that of NS1643. Moreover, the most interesting compounds were further investigated by synthesizing in a parallel fashion some analogs, with the aim to get insights about structure-activity relationships.

Modern methods of drug discovery and development in recent years make a wide use of computational algorithms. These methods utilise Virtual Screening (VS), which is the computational counterpart of experimental screening. In this manner the in silico models and tools initial replace the wet lab methods saving time and resources. This paper presents the overall design and implementation of a web based scientific workflow system for virtual screening called, the Life Sciences Informatics (LiSIs) platform. The LiSIs platform consists of the following layers: the input layer covering the data file input; the pre-processing layer covering the descriptors calculation, and the docking preparation components; the processing layer covering the attribute filtering, compound similarity, substructure matching, docking prediction, predictive modelling and molecular clustering; post-processing layer covering the output reformatting and binary file merging components; output layer covering the storage component. The potential of LiSIs platform has been demonstrated through two case studies designed to illustrate the preparation of tools for the identification of promising chemical structures. The first case study involved the development of a Quantitative Structure Activity Relationship (QSAR) model on a literature dataset while the second case study implemented a docking-based virtual screening experiment. Our results show that VS workflows utilizing docking, predictive models and other in silico tools as implemented in the LiSIs platform can identify compounds in line with expert expectations. We anticipate that the deployment of LiSIs, as currently implemented and available for use, can enable drug discovery researchers to more easily use state of the art computational techniques in their search for promising chemical compounds. The LiSIs platform is freely accessible (i) under the GRANATUM platform at: http://www.granatum.org and (ii) directly at: http://lisis.cs.ucy.ac.cy.

Idiosyncratic drug reactions (IDRs) are rare, somewhat dose-independent, patient-specific and hard to predict. Human leukocyte antigens (HLAs) are the major histocompatibility complex (MHC) in humans, are highly polymorphic and are associated with specific IDRs. Therefore, it is important to identify potential drug-HLA associations so that individuals who would develop IDRs can be identified before drug exposure. We harvested the associations between drugs and class I HLAs from the literature. The results revealed that there are many drug-HLA pairs without clinical data. For better potential interactions of the drug-HLA pairs, molecular docking was used to explore the potential of associations between the drugs and HLAs. From the analysis of docking scores between the 17 drugs and 74 class I HLAs, it was observed that the known significantly associated drug-HLA pairs had statistically lower docking scores than those not reported to be significantly associated (t-test p < 0.05). This indicates that molecular docking could be utilized for screening drug-HLA interactions and predicting potential IDRs. Examining the binding modes of drugs in the docked HLAs suggested several distinct binding sites inside class I HLAs, expanding our knowledge of the underlying interaction mechanisms between drugs and HLAs.

Resistance of bacteria to current antibiotics is an alarming health problem. In this sense, Pseudomonas represents a genus of Gram-negative pathogens, which has emerged as one of the most dangerous species causing nosocomial infections. Despite the effort of the scientific community, drug resistant strains of bacteria belonging to Pseudomonas spp. prevail. The high costs associated to drug discovery and the urgent need for more efficient antimicrobial chemotherapies envisage the fact that computeraided methods can rationalize several stages involved in the development of a new drug. In this work, we introduce a chemoinformatic methodology devoted to the construction of a multitasking model for quantitative-structure biological effect relationships (mtk-QSBER). The purpose of this model was to perform simultaneous predictions of anti-Pseudomonas activities and ADMET (absorption, distribution, metabolism, elimination, and toxicity) properties of organic compounds. The mtk-QSBER model was created from a large and heterogeneous dataset (more than 54000 cases) and displayed accuracies higher than 90% in both training and prediction sets. In order to demonstrate the applicability of our mtk-QSBER model, we used the investigational antibacterial drug delafloxacin as a case of study, for which experimental results were recently reported. The predictions performed for many biological effects of this drug exhibited a remarkable convergence with the experimental assays, confirming that our model can serve as useful tool for virtual screening of potent and safer anti-Pseudomonas agents.

In this paper we report quantitative structure-activity models linking in vivo Drug-Induced Liver Injury (DILI) of organic molecules with some parameters both measured experimentally in vitro and calculated theoretically from the molecular structure. At the first step, a small database containing information of DILI in humans was created and annotated by experimentally observed information concerning hepatotoxic effects. Thus, for each compound a binary annotation “yes/no” was applied to DILI and seven endpoints causing different liver pathologies in humans: Cholestasis (CH), Oxidative Stress (OS), Mitochondrial injury (MT), Cirrhosis and Steatosis (CS), Hepatitis (HS), Hepatocellular (HC), and Reactive Metabolite (RM). Different machine-learning methods were used to build classification models linking DILI with molecular structure: Support Vector Machines, Artificial Neural Networks and Random Forests. Three types of models were developed: (i) involving molecular descriptors calculated directly from chemical structure, (ii) involving selected endpoints as “biological” descriptors, and (iii) involving both types of descriptors. It has been found that the models based solely on molecular descriptors have much weaker prediction performance than those involving in vivo measured endpoints. Taking into account difficulties in obtaining of in vivo data, at the validation stage we used instead five endpoints (CH, CS, HC, MT and OS) measured in vitro in human hepatocyte cultures. The models involving either some of experimental in vitro endpoints or their combination with theoretically calculated ones correctly predict DILI for 9 out of 10 reference compounds of the external test set. This opens an interesting perspective to use for DILI predictions a combination of theoretically calculated parameters and measured in vitro biological data.

The increase of publicly available bioactivity data has led to the extensive development and usage of in silico bioactivity prediction algorithms. A particularly popular approach for such analyses is the multiclass Naïve Bayes, whose output is commonly processed by applying empirically-derived likelihood score thresholds. In this work, we describe a systematic way for deriving score cut-offs on a per-protein target basis and compare their performance with global thresholds on a large scale using both 5-fold cross-validation (ChEMBL 14, 189k ligand-protein pairs over 477 protein targets) and external validation (WOMBAT, 63k pairs, 421 targets). The individual protein target cut-offs derived were compared to global cut-offs ranging from -10 to 40 in score bouts of 2.5. The results indicate that individual thresholds had equal or better performance in all comparisons with global thresholds, ranging from 95% of protein targets to 57.96%. It is shown that local thresholds behave differently for particular families of targets (CYPs, GPCRs, Kinases and TFs). Furthermore, we demonstrate the discrepancy in performance when we move away from the training dataset chemical space, using Tanimoto similarity as a metric (from 0 to 1 in steps of 0.2). Finally, the individual protein score cut-offs derived for the in silico bioactivity application used in this work are released, as well as the reproducible and transferable KNIME workflows used to carry out the analysis.