In silico physicochemical parameter predictions

Non-clinical investigations about cytotoxic and anti-platelet activities of gamma-terpinene

Gamma-terpinene (γ-TPN) is a cyclohexane monoterpene isolated from plant essential oils, such as tea tree (Melaleuca alternifolia), oregano (Origanum vulgare), rosemary (Rosmarinus officinalis L.), thyme (Thymus vulgaris Marchand), and eucalyptus (Eucalyptus sp.). Terpenes are widely studied molecules pharmacologically active on the cardiovascular system, hemostasis, and antioxidant actions. Herein, it was investigated the cytotoxic and antiplatelet activity of γ-TPN using different non-clinical laboratory models. For in silico evaluation, the PreADMET, SwissADME, and SwissTargetPrediction softwares were used. Molecular docking was performed using the AutoDockVina and BIOVIA Discovery Studio databases. The cytotoxicity of γ-TPN was analyzed by the MTT assay upon normal murine endothelial SVEC4-10 and fibroblast L-929 cells. Platelet aggregation was evaluated with platelet-rich (PRP) and platelet-poor (PPP) plasma from spontaneously hypertensive rats (SHR), in addition to SVEC4-10 cells pre-incubated with γ-TPN (50, 100, and 200 µM) for 24 h. SHR animals were pre-treated by gavage with γ-TPN for 7 days and divided into four groups (negative control, 25, 50, and 100 mg/kg). Blood samples were collected to measure nitrite using the Griess reagent. Gamma-TPN proved to be quite lipid-soluble (Log P = +4.50), with a qualified profile of similarity to the drug, good bioavailability, and adequate pharmacokinetics. It exhibited affinity mainly for the P2Y12 receptor (6.450 ± 0.232 Kcal/mol), moderate cytotoxicity for L-929 (CC50 = 333.3 µM) and SVEC 4-10 (CC50 = 366.7 µM) cells. The presence of γ-TPN in SVEC 4-10 cells was also able to reduce platelet aggregation by 51.57 and 44.20% at lower concentrations (50 and 100 µM, respectively). Then, γ-TPN has good affinity with purinergic receptors and an effect on the reversal of platelet aggregation and oxidative stress, being promising and safe for therapeutic targets and subsequent studies on the control of thromboembolic diseases.

Identification of novel sulfathiazole-triazolo-chalcone hybrids as VEGFR-2/EGFR dual inhibitors with antiangiogenic activity and apoptotic induction

Certain sulfathiazole-triazolo chalcone hybrids were identified as anticancer agents with dual vascular endothelial growth factor receptor-2 (VEGFR-2)/epidermal growth factor receptor (EGFR) kinase inhibitory effect. All of the compounds were evaluated for their cytotoxic activity against the MCF-7 and HepG-2 tumor cell lines. Compounds 11g, 11h, and 11j exhibited the most potent antiproliferative activity against both cancer cell lines, with good safety toward WI-38 normal cells. Thus, they were further assessed for VEGFR-2 inhibitory activity. They have suppressed VEGFR-2 enzyme at IC50 of 0.316, 0.076, and 0.189 µM, respectively in comparison to sorafenib (IC50  = 0.035 µM). EGFR enzyme inhibition was further screened for the most potent inhibitors, 11h and 11j, where they displayed enhanced potency with IC50 of 0.085 and 0.108 µM, respectively, compared to erlotinib (IC50  = 0.037 µM). Compounds 11h and 11j were additionally investigated for inhibition of comparable kinases, PDGFR-β and B-Raf, where results assessed adequate selectivity of both compounds toward the VEGFR-2 and EGFR kinases. Furthermore, the wound healing assay of compound 11h manifested a percent wound closure of 65.18% in MCF-7 cells compared to doxorubicin (58.51%) and untreated cells (97.77%), proving its antiangiogenic activity. The cell cycle assay of MCF-7 cells treated with 11h demonstrated cell cycle arrest at the S phase. Moreover, compound 11h induced apoptosis with a 44-fold increase compared to that induced in the control MCF-7 cells. Molecular docking results of compounds 11h and 11j established their efficacies, and in silico studies showed convenient safety profiles with drug-likeness properties.

Challenging the anticolorectal cancer capacity of quinoxaline-based scaffold via triazole ligation unveiled new efficient dual VEGFR-2/MAO-B inhibitors

Monoamine oxidases (MAOs) and vascular endothelial growth factor receptor-2 (VEGFR-2) are promoters of colorectal cancer (CRC) and central signaling nodes in epithelial-mesenchymal transition (EMT) induced by activating hypoxia-inducible factors (HIFs). Herein, a novel series of rationally designed triazole-tethered quinoxalines were synthesized and evaluated against HCT-116 CRC cells. The tailored scaffolds combine the pharmacophoric themes of both VEGFR-2 inhibitors and MAO inhibitors. All the synthesized derivatives were screened utilizing the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay for their possible cytotoxic effects on normal human colonocytes, then evaluated for their anticancer activities against HCT-116 cells overexpressing MAOs. The hit derivatives 11 and 14 exhibited IC50 = 18.04 and 7.850 µM, respectively, against HCT-116cells within their EC100 doses on normal human colonocytes. Wound healing assay revealed their efficient CRC antimetastatic activities recording HCT-116 cell migration inhibition exceeding 75 %. In vitro enzymatic assays demonstrated that both 11 and 14 efficiently inhibited VEGFR-2 (IC50 = 88.79 and 9.910 nM), MAO-A (IC50 = 0.763 and 629.1 nM) and MAO-B (IC50 = 0.488 and 209.6 nM) with observed MAO-B over MAO-A selectivity (SI = 1.546 and 3.001), respectively. Enzyme kinetics studies were performed for both compounds to identify their mode of MAO-B inhibition. Furthermore, qRT-PCR analysis showed that the hits efficiently downregulated HIF-1α in HCT-116cells by 3.420 and 16.96 folds relative to untreated cells. Docking studies simulated their possible binding modes within the active sites of VEGFR-2 and MAO-B to highlight their essential structural determinants of activities. Finally, they recorded in silico drug-like absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles as well as ligand efficiency metrics.

Intrinsic Solubility of Ionizable Compounds from pKa Shift

Aqueous solubility of pharmaceutical substances plays an important role in small molecule drug discovery and development, with ionizable groups often employed to enhance solubility. Drug candidate compounds often contain ionizable groups to increase their solubility. Recognizing that the electrostatically charged form of the compound is much more soluble than the uncharged form, this work proposes a model to explore the relationship between the pKa shift of the ionizable group and dissolution equilibria. The model considers three forms of a compound: dissolved-charged, dissolved-uncharged, and aggregated-uncharged. It analyzes two linked equilibria: the protonation of the ionizable group and the dissolution-aggregation of the uncharged form, with the observed pKa shift depending on the total concentration of the compound. The active concentration of the aggregates determines this shift. The model was explored through the determination of the pKa shift and intrinsic solubility of specific compounds, such as ICPD47, a high-affinity inhibitor of the Hsp90 chaperone protein and anticancer target, as well as benzoic acid and benzydamine. The model holds the potential for a more nuanced understanding of intrinsic solubility and may lead to advancements in drug discovery and development.

In silico evaluation of binding interaction and ADME study of new 1,3-diazetidin-2-one derivatives with high antiproliferative activity

A series of eight novels' 1,3-diazetidin-2-ones have been proposed to assess their potential activities. They are intended to examine antiproliferative effects through inhibition of epidermal growth factor receptor (EGFR) expression. These eight compounds strongly interact with the EGFR protein, responsible for the activity. As part of a present study, these compounds were docked to the crystal structure of the EGFR (Protein Data Bank code: 1 M17) to determine their binding affinity at the active site. Based on computer predictions, two compounds were demonstrated high scores of 80.80 and 85.89. After analyzing ADME properties, these compounds were found to have significant potential for binding. Consequently, the abilities of gefitinib, erlotinib, imatinib, and sorafenib were selected for comparison as controls. Computational methods were performed to predict the critical disposition of eight novels' 1,3-diazetidin-2-one derivatives to the EGFR. Moreover, a docking technique employing the Genetic Optimization for Ligand Docking program was conducted. Compounds 2 and 7 demonstrate a high docking peace-wise scoring function (PLP) fitness of 85.89 and 80.80, respectively. They fulfilled the Lipinski's rule, topological descriptors, and fingerprints of drug-like molecular structure keys. These compounds can be used as lead compounds to develop novel antiproliferative agents. The outcome of applying this study is novel series of 1,3-diazetidin-2-one compounds as new analogs were designed and evaluated for their antiproliferative activity with a higher potency profile and binding affinity within the active sites of EGFR.

Co-amorphous formulation of dipyridamole with p-hydroxybenzoic acid: Underlying molecular mechanisms, physical stability, dissolution behavior and pharmacokinetic study

Coamorphization has been proven to be an effective approach to improve bioavailability of poorly soluble active pharmaceutical ingredients (APIs) by virtue of solubilization, and also contributes to overcome limitation of physical stability associated with amorphous drug alone. In current work, a co-amorphous formulation of dipyridamole (DPM), a poor solubility drug, with p-hydroxybenzoic acid (HBA) was prepared and investigated. At a molar ratio of 1:2, DPM and HBA were melted result in the formation of a binary co-amorphous system. The DPM-HBA co-amorphous was structurally characterized by powder X-ray diffraction (PXRD), temperature modulated differential scanning calorimetry (mDSC), high performance liquid chromatography (HPLC) and solution state ¹H nuclear magnetic resonance (¹H NMR). The molecular mechanisms in the co-amorphous were further analysed via Fourier-transform infrared (FTIR) and Raman spectroscopies, as well as density functional theory (DFT) calculation. All the results consistently revealed the presence of hydrogen bonding interactions between -OH of DPM and -COOH on HBA. Accelerated test and glass transition kinetics showed excellent physical stability of DPM-HBA co-amorphous compared with amorphous DPM along with glass transition temperatures (T_g). The phase-solubility study indicated that complexation occurred between DPM and HBA in solution, which contributed to the solubility and dissolution enhancement of DPM in co-amorphous system. Pharmacokinetic study of co-amorphous DPM-HBA in mouse plasma revealed that the DPM exhibited 1.78-fold and 2.64-fold improvement in AUC_0‑∞ value compared with crystalline and amorphous DPM, respectively. This current study revealed coamorphization is an effective approach for DPM to improve the solubility and biopharmaceutical performance.

Design of a Stable Coamorphous System Using Lactose as an Antiplasticizing Agent for Diphenhydramine Hydrochloride with a Low Glass Transition Temperature

Coamorphous systems comprising small molecules are emerging as counterparts to polymeric solid dispersions. However, the glass transition temperatures (T_gs) of coamorphous materials are relatively low because of the lack of polymeric carriers with higher T_gs. This study aimed to investigate the applicability of lactose (LAC) as an antiplasticizing coformer to a coamorphous system. Diphenhydramine hydrochloride (DPH) was selected as a model drug (T_g = 16 °C). Differential scanning calorimetry showed a comelting point in addition to a decrease in the neat melting points depending on the composition of the physical mixtures, suggesting that the mixture of DPH-LAC was eutectic. The melting point of the eutectic mixture was calculated according to the Schröder-van Laar equation. The heat of fusion of the eutectic mixture was maximized at a 70:30 molar ratio of DPH to LAC; at this point, the melting peaks of the pure components disappeared. The heat flow profiles following the melting and cooling of DPH-LAC physical mixtures at the ratios from 10:90 to 90:10 showed a single T_g, suggesting the formation of a coamorphous system. Lactose showed a T_g of over 100 °C, and the T_g of DPH increased with the molar ratio of LAC; it was 84 °C at a 10:90 molar ratio of DPH to LAC. The Raman image indicated the formation of a homogeneous dispersion of DPH and LAC in the coamorphous system. Peak shifts in the infrared spectra indicated the presence of intermolecular interactions between the amino group of DPH and the hydroxyl group of LAC. Principal component analysis of the infrared spectra revealed a significant change at the 70:30 molar ratio of DPH to LAC, which was in agreement with the results of the thermal analysis. A stability test at 40 °C revealed rapid crystallization of the supercooled liquid DPH. The coamorphous samples containing 10-50% of LAC remained in an amorphous state for 21 days, and no crystallization was observed for the samples containing >60% of LAC for 28 days. The relatively lower T_g (less than 40 °C) of the coamorphous system containing 10-50% of LAC might have caused crystallization during storage. These findings indicate that LAC, which is a safe and widely used pharmaceutical excipient, can be applied to coamorphous systems as an antiplasticizing coformer.

Recent advancement in drug development of nitro(NO2 )-heterocyclic compounds as lead scaffolds for the treatment of Mycobacterium tuberculosis

Tuberculosis (TB) is an infectious disease caused predominantly by Mycobacterium tuberculosis (Mtb). It was responsible for approximately 1.4 million deaths worldwide in 2019. The lack of new drugs to treat drug-resistant strains is a principal factor for the slow rise in TB infections. Our aim is to aid the development of new TB treatments by describing improvements (last decade, 2011-2021) to nitro(NO₂ )-based compounds that have shown activity or pharmacological properties (e.g., anti-proliferative, anti-kinetoplastid) against Mtb. For all compounds, we have included final correlations of minimum inhibitory concentrations against Mtb (H₃₇ Rv).

Cheminformatic Characterization of Natural Antimicrobial Products for the Development of New Lead Compounds

The growing antimicrobial resistance (AMR) of pathogenic organisms to currently prescribed drugs has resulted in the failure to treat various infections caused by these superbugs. Therefore, to keep pace with the increasing drug resistance, there is a pressing need for novel antimicrobial agents, especially from non-conventional sources. Several natural products (NPs) have been shown to display promising in vitro activities against multidrug-resistant pathogens. Still, only a few of these compounds have been studied as prospective drug candidates. This may be due to the expensive and time-consuming process of conducting important studies on these compounds. The present review focuses on applying cheminformatics strategies to characterize, prioritize, and optimize NPs to develop new lead compounds against antimicrobial resistance pathogens. Moreover, case studies where these strategies have been used to identify potential drug candidates, including a few selected open-access tools commonly used for these studies, are briefly outlined.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Tautomer Standardization in Chemical Databases: Deriving Business Rules from Quantum Chemistry

Databases of small, potentially bioactive molecules are ubiquitous across the industry and academia. Designed such that each unique compound should appear only once, the multiplicity of ways in which many compounds can be represented means that these databases require methods for standardizing the representation of chemistry. This is commonly achieved through the use of "Chemistry Business Rules", sets of predefined rules that describe the "house style" of the database in question. At Syngenta, the historical approach to the design of chemistry business rules has been to focus on consistency of representation, with chemical relevance given secondary consideration. In this work, we overturn that convention. Through the use of quantum chemistry calculations, we define a set of chemistry business rules for tautomer standardization that reproduces gas-phase energetic preferences. We go on to show that, compared to our historic approach, this method yields tautomers that are in better agreement with those observed experimentally in condensed phases and that are better suited for use in predictive models.

A general linear free energy relationship for predicting partition coefficients of neutral organic compounds

Predicting the effects of organic compounds on environments and biological systems is an important objective for environmental chemistry and human health. The logarithm (to base 10) of the n-octanoll-water partition coefficient has been widely used to predict the mentioned properties. However, the suitability of this parameter for the purpose has been questioned, since the environments relating to the properties may be quite different from that of bulk n-octanol. In this study, we used a theoretical derivation approach to develop a model for predicting the partition coefficients of solutes between water and an organic solvent that may be similar to n-octanol or quite different from it. Our model relies on solute descriptors that can be calculated based on solute structures. It was used to predict the n-octanoll-water, hexadecanel-water and chloroforml-water partition coefficients of solutes. The calculated values of the examined parameters agreed well with their experimental counterparts. The model can find application in the accurate prediction of the effects of organic compounds on environments and the physicochemical properties of organic compounds by a full in-silico approach and can provide useful guidance for improving some properties of organic compounds.

Prediction of paclitaxel pharmacokinetic based on in vitro studies: Interaction with membrane models and human serum albumin

Interactions of paclitaxel (PTX) with models mimicking biological interfaces (lipid membranes and serum albumin, HSA) were investigated to test the hypothesis that the set of in vitro assays proposed can be used to predict some aspects of drug pharmacokinetics (PK). PTX membrane partitioning was studied by derivative spectrophotometry; PTX effect on membrane biophysics was evaluated by dynamic light scattering, fluorescence anisotropy, atomic force microscopy and synchrotron small/wide-angle X-ray scattering; PTX distribution/molecular orientation in membranes was assessed by steady-state/time-resolved fluorescence and computer simulations. PTX binding to HSA was studied by fluorescence quenching, derivative spectrophotometry and dynamic/electrophoretic light scattering. PTX high membrane partitioning is consistent with its efficacy crossing cellular membranes and its off-target distribution. PTX is closely located in the membrane phospholipids headgroups, also interacting with the hydrophobic chains, and causes a major distortion of the alignment of the membrane phospholipids, which, together with its fluidizing effect, justifies some of its cellular toxic effects. PTX binds strongly to HSA, which is consistent with its reduced distribution in target tissues and toxicity by bioaccumulation. In conclusion, the described set of biomimetic models and techniques has the potential for early prediction of PK issues, alerting for the required drug optimizations, potentially minimizing the number of animal tests used in the drug development process.

Chemical Patterns of Proteasome Inhibitors: Lessons Learned from Two Decades of Drug Design

Drug discovery now faces a new challenge, where the availability of experimental data is no longer the limiting step, and instead, making sense of the data has gained a new level of importance, propelled by the extensive incorporation of cheminformatics and bioinformatics methodologies into the drug discovery and development pipeline. These enable, for example, the inference of structure-activity relationships that can be useful in the discovery of new drug candidates. One of the therapeutic applications that could benefit from this type of data mining is proteasome inhibition, given that multiple compounds have been designed and tested for the last 20 years, and this collection of data is yet to be subjected to such type of assessment. This study presents a retrospective overview of two decades of proteasome inhibitors development (680 compounds), in order to gather what could be learned from them and apply this knowledge to any future drug discovery on this subject. Our analysis focused on how different chemical descriptors coupled with statistical tools can be used to extract interesting patterns of activity. Multiple instances of the structure-activity relationship were observed in this dataset, either for isolated molecular descriptors (e.g., molecular refractivity and topological polar surface area) as well as scaffold similarity or chemical space overlap. Building a decision tree allowed the identification of two meaningful decision rules that describe the chemical parameters associated with high activity. Additionally, a characterization of the prevalence of key functional groups gives insight into global patterns followed in drug discovery projects, and highlights some systematically underexplored parts of the chemical space. The various chemical patterns identified provided useful insight that can be applied in future drug discovery projects, and give an overview of what has been done so far.

Aqueous Drug Solubility: What Do We Measure, Calculate and QSPR Predict?

Detailed critical analysis of publications devoted to QSPR of aqueous solubility is presented in the review with discussion of four types of aqueous solubility (three different thermodynamic solubilities with unknown solute structure, intrinsic solubility, solubility in physiological media at pH=7.4 and kinetic solubility), variety of molecular descriptors (from topological to quantum chemical), traditional statistical and machine learning methods as well as original QSPR models.

Design of Novel Oligomeric Mixed Ligand Complexes: Preparation, Biological Applications and the First Example of Their Nanosized Scale

A successful oligomerization of ternary metal complexes, cobalt (II), nickel (II), copper (II), zinc (II), chromium (III) and ferric sulfate (III) with nitrilotriacetic acid (NTA) as a primary ligand and glutamic acid as a secondary ligand, has been demonstrated. The formation of oligomers arose from the presence of the sulfate moiety, which operates as a bridged bidentate ligand that coordinates with other metal moieties. The novel oligomers exhibited octahedral structures, which bonded together through the sulfate moiety. In silico predictions were conducted to gauge the bioactivity, physico-chemical and pharmacokinetic properties. The biological activities of these oligomers as well as their tumor inhibitory behavior have been explored. This work also presents a facile and novel method of preparing these materials in nanosize, using Cetyltrimethylammonium bromide (CTAB) and polyvinyl alcohol (PVA) as capping ligands. The size and shape of the nanomaterials have been confirmed using the transmission electron microscope (TEM) and the scanning electron microscope (SEM).

Thiazole, thio and semicarbazone derivatives against tropical infective diseases: Chagas disease, human African trypanosomiasis (HAT), leishmaniasis, and malaria

Thiazole, thiosemicarbazone and semicarbazone moieties are privileged scaffolds (acting as primary pharmacophores) in many compounds that are useful to treat several diseases, mainly tropical infectious diseases. In this review article, we critically analyzed the contribution of these scaffolds to medicinal chemistry in the last five years, focusing on tropical infectious diseases, such as Chagas disease, human African trypanosomiasis (HAT), leishmaniasis, and malaria. We also present perspectives for their use in drug design in order to contribute to the development of new drugs.

Current advances in drug discovery for Chagas disease

Chagas disease, also known as American trypanosomiasis, is one of the 17 neglected tropical diseases (NTDs) according to World Health Organization. It is estimated that 8-10 million people are infected worldwide, mainly in Latin America. Chagas disease is caused by the parasite Trypanosoma cruzi and is characterized by two phases: acute and chronic. The current therapy for Chagas disease is limited to drugs such as nifurtimox and benznidazole, which are effective in treating only the acute phase of the disease. In addition, several side effects ranging from hypersensitivity to bone marrow depression and peripheral polyneuropathy have been associated with these drugs. Therefore, the current challenge is to find new effective and safe drugs against this NTD. The aim of this review is to describe the advances in the medicinal chemistry of new anti-chagasic compounds reported in the literature in the last five years. We report promising prototypes for drug discovery identified through target-based and phenotype-based strategies and present some important targets for the development of new synthetic compounds.

Exploring the novel heterocyclic derivatives as lead molecules for design and development of potent anticancer agents

This paper deals with in silico evaluation of newly proposed heterocyclic derivatives in search of potential anticancer activity. Best possible drug candidates have been proposed using a rational approach employing a pipeline of computational techniques namely MetaPrint2D prediction, molinspiration, cheminformatics, Osiris Data warrior, AutoDock and iGEMDOCK. Lazar toxicity prediction, AdmetSAR predictions, and targeted docking studies were also performed. 27 heterocyclic derivatives were selected for bioactivity prediction and drug likeness score on the basis of Lipinski's rule, Viber rule, Ghose filter, leadlikeness and Pan Assay Interference Compounds (PAINS) rule. Bufuralol, Sunitinib, and Doxorubicin were selected as reference standard drug for the comparison of molecular descriptors and docking. Bufuralol is a known non-selective adreno-receptor blocking agent. Studies showed that beta blockers are also used against different types of cancers. Sunitinib is well known Food and Drug administration (FDA) approved pyrrole containing tyrosine kinase inhibitor and our proposed molecules possess similarities with both drug and doxorubicin is another moiety having anticancer activity. All heterocyclic derivatives were found to obey the drug filters except standard drug Doxorubicin. Bioactivity score of the compounds was predicted for drug targets including enzymes, nuclear receptors, kinase inhibitors, G protein-coupled receptor (GPCR) ligands and ion channel modulators. Absorption, distribution, metabolism and toxicity (ADMET) prediction of all proposed compound showed good Blood-brain barrier (BBB) penetration, Human intestinal absorption (HIA), Caco-2 cell permeability except compound-11 and was found to have no AdmetSAR toxicity as well as carcinogenic effect. Compounds 1-9 were slightly mutagenic while compound 2, 11, 20 and 21 showed carcinogenic effect according to Lazar toxicity prediction. Rests of the compounds were predicted to have no side effect. Molecular docking was performed with vascular endothelial growth factor receptor-2(VEGFR2) and glutathione S-transferase-1 (GSTP1) because both are common cancer causing proteins. Sunitinib and Doxorubicin possess great affinity to inhibit these cancers causing protein. Self-organizing map (SOM) was used to depict data in a simple 2D presentation. Our studies justify that good oral bioavailability and therapeutic efficacy of 10, 12-19 and 22-27 compounds can be considered as potential anticancer agents.

Oral drug suitability parameters

Assessing the oral drug suitability of compounds as early as possible within drug discovery is an important objective. This study describes a methodology that attempts to simplify the evaluation of compounds based on their in vivo quantity levels within a mammalian body, represented using a mathematical model that imposes a time limitation on oral absorption and assumes non-instantaneous drug distribution between plasma and tissue. This simplification results in two new oral drug suitability parameters that can quantitatively relate oral dose to in vivo exposure for compounds with vastly different tendencies in terms of absorption into, and elimination from, the body. Consequently, the complexities associated with evaluating a compound's oral drug suitability are simplified to an assessment of these two new parameters. Application of this methodology at the virtual design stage is discussed, along with functionality that accounts for uncertainty related to a compound's distribution kinetics and errors associated to in silico QSAR predictions for the required input data.

Harnessing the Properties of Natural Products

Natural products (NPs) have been used as traditional medicines since antiquity. With more than 10⁶⁰ estimated compounds with molecular weights less than 500 Da representing chemical space, NPs occupy a very small percentage; however, they are significantly overrepresented in biologically relevant chemical space. The classical approach concentrates on identifying one or more NPs with biological activity from a source organism. There is much more to be learned from NPs than we can discover this narrow view. In this review, we discuss ways to harness the global properties of NPs.

Advances in Drug Discovery of New Antitubercular Multidrug-Resistant Compounds

Tuberculosis (TB), a disease caused mainly by the Mycobacterium tuberculosis (Mtb), is according to the World Health Organization (WHO) the infectious disease responsible for the highest number of deaths worldwide. The increased number of multidrug-resistant (MDR-TB) and extensively drug-resistant (XDR-TB) strains, and the ineffectiveness of the current treatment against latent tuberculosis are challenges to be overcome in the coming years. The scenario of drug discovery becomes alarming when it is considered that the number of new drugs does not increase proportionally to the emergence of drug resistance. In this review, we will demonstrate the current advances in antitubercular drug discovery, focusing on the research of compounds with potent antituberculosis activity against MDR-TB strains. Herein, active compounds against MDR-TB with minimum inhibitory concentrations (MICs) less than 11 µM and low toxicity published in the last 4 years in the databases PubMed, Web of Science and Scopus will be presented and discussed.

Designing safer oral drugs

Designing an oral drug such that its estimated dose to humans is both efficacious and safe is challenging. During the early design stage, where in vitro or preclinical species in vivo safety data are limited, heuristic-based criteria often related to physicochemical properties are used for guidance. The causal relationship between a compound's log P and its human in vivo toxicity is considered. With respect to designing efficacious oral drugs that potentially have reduced toxicity liabilities, an alternative heuristic-based criterion is proposed based on the amount of compound in the body at steady state. In humans, a threshold for the amount of compound in the body at steady state of 0.5 mg kg-1 is suggested. The criterion is based on the minimum toxic blood-plasma concentration that produces clinically relevant side effects or symptoms in humans for 242 oral drugs. It can be used to estimate a therapeutic window against which a compound's estimated in vivo plasma levels for a particular dose size and frequency can be assessed. The relationship between this criterion and acceptable oral dose sizes for different charge types with different in vivo plasma clearances is discussed.

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

Profiling the estimated plasma concentrations of 215 marketed oral drugs

The human pharmacokinetic parameters of 215 marketed oral drugs have been collated and their estimated plasma concentrations (following repeat dosing) profiled against time using a one-compartment model.

Interpreting physicochemical experimental data sets

With the wealth of experimental physicochemical data available to chemoinformaticians from the literature, commercial, and company databases an increasing challenge is the interpretation of such datasets. Subtle differences in experimental methodology used to generate these datasets can give rise to variations in physicochemical property values. Such methodology nuances will be apparent to an expert experimentalist but not necessarily to the data analyst and modeller. This paper describes the differences between common methodologies for measuring the four most important physicochemical properties namely aqueous solubility, octan-1-ol/water distribution coefficient, pK(a) and plasma protein binding highlighting key factors that can lead to systematic differences. Insight is given into how to identify datasets suitable for combining.

A new approach to radial basis function approximation and its application to QSAR

We describe a novel approach to RBF approximation, which combines two new elements: (1) linear radial basis functions and (2) weighting the model by each descriptor's contribution. Linear radial basis functions allow one to achieve more accurate predictions for diverse data sets. Taking into account the contribution of each descriptor produces more accurate similarity values used for model development. The method was validated on 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. We also compared the new method with five different QSAR methods implemented in the EPA T.E.S.T. program. Our approach, implemented in the program GUSAR, showed a reasonable accuracy of prediction and high coverage for all external test sets, providing more accurate prediction results than the comparison methods and even the consensus of these methods. Using our new method, we have created models for physicochemical and toxicity endpoints, which we have made freely available in the form of an online service at http://cactus.nci.nih.gov/chemical/apps/cap.

N-acylated derivatives of sulfamethoxazole and sulfafurazole inhibit intracellular growth of Chlamydia trachomatis

Antibacterial compounds with novel modes of action are needed for management of bacterial infections. Here we describe a high-content screen of 9,800 compounds identifying acylated sulfonamides as novel growth inhibitors of the sexually transmitted pathogen Chlamydia trachomatis. The effect was bactericidal and distinct from that of sulfonamide antibiotics, as para-aminobenzoic acid did not reduce efficacy. Chemical inhibitors play an important role in Chlamydia research as probes of potential targets and as drug development starting points.