PASS Targets: Ligand-based multi-target computational system based on a public data and naïve Bayes approach

Modulatory effect of metformin and its transporters on immune infiltration in tumor microenvironment: a bioinformatic study with experimental validation

Metformin is a traditional antidiabetic drug for type 2 diabetes mellitus. However, it showed antitumor activity in many types of tumors, and it also has an influence on tumor metastasis in several types of tumors. It is transported through organic cationic transporters (OCTs), OCT1, OCT2, and OCT3, into the cells or into tumor microenvironment (TME). The complex interaction of metformin and its transporters on immune infiltration in TME of different types of tumors of The Cancer Genomic Atlas (TCGA) is not yet studied. The objective of this study is to identify the most suitable therapeutic target of tumors and immune infiltrates for metformin and its transporters in the TME. TIMER2.0, a bioinformatic tool, and other computational analysis were used to investigate this complex interaction; moreover, the identification of metformin target protein in TME is also investigated. The results revealed that the most suitable therapeutic target for metformin and OCTs among 32 types of TCGA data tumor types is Breast Invasive carcinoma (BRCA), and the most relevant immune infiltrate among 14 types of immune infiltrates that yields better prognosis and better therapeutical effect in TME is Macrophage M1. Furthermore, metformin showed a cytotoxic effect and an inhibitory effect on Urokinase Plasminogen Activator (uPA) gene expression in a concentration dependent fashion in MDA-MB-231 breast cancer cell line. This may suggest that metformin is a promising antitumor drug, stimulant for natural antitumor immune infiltrates, and inhibitor for metastasis in breast cancer.

Validating the antidiabetic potential of Nakima (Tupistra clarkei Hook.f.), a traditional food from eastern Himalayan region, through network pharmacology and in vivo experimentation

OBJECTIVE

To explore and understand the antidiabetic activity of Tupistra clarkei Hook.f. inflorescence, providing a scientific explanation to the ethnomedicinal properties.

METHODS

The constituents of the plant were determined through GC-MS analysis, which were used for target prediction and network pharmacology to understand how the plant regulates hyperglycaemia and other diabetes complications. These properties were validated in vivo along with further assessment of the antioxidant potential of the plant, both in vitro and in vivo.

KEY FINDINGS

The plant showed good phenol-flavonoid content, and antioxidant potential both in vitro and in vivo. GC-MS analysis identified 24 constituents of the plant. In silico analysis showed their ability to target 166 proteins that are associated with pathways in controlling hyperglycaemia and other diabetic consequences, protection of pancreatic tissue, insulin secretion, and insulin resistance. This was reflected in the in vivo experiment where T. clarkei showed ability to reduce FBG, LDL-C, VLDL-C levels, improve the levels of HDL-C, and also facilitate reversal of damage in pancreatic islets.

CONCLUSION

Our study validated the antidiabetic potential Tupistra clarkei inflorescence in the in silico and in vivo assessment, and has proved to have good antioxidant activity and potential against diabetes. However, further clinical trials are essential.

Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

AmIActive (AIA): A Large-scale QSAR Based Target Fishing and Polypharmacology Predictive Web Tool

Here, we introduce AmIActive (AIA), a QSAR-based web tool for biological activity prediction and target fishing. The AIA system uses QSAR models trained with biological activity data from the ChEMBL database and currently covers single proteins, protein complexes, protein families, cell lines, organisms, and tissues. The system contains 3,239 models corresponding to 2,277 distinct targets of interest. These models are Random Forest classifiers trained with circular fingerprint descriptors, and all models have clearly defined applicability domains. The system is easy to use, with a friendly and intuitive interface. The results can be downloaded in CSV format and include predictive information, details about the target, validation metrics for the models, and the active/inactive threshold used to train the classifier. The AIA system is available free of charge and can be accessed at the following link: https://amiactive.ccen.ufpb.br/.

Fifteen years of ChEMBL and its role in cheminformatics and drug discovery

In October 2024 we celebrated the 15th anniversary of the first launch of ChEMBL, Europe's most impactful, open-access drug discovery database, hosted by EMBL's European Bioinformatics Institute (EMBL-EBI). This is a good moment to reflect on ChEMBL's history, the role that ChEMBL plays in Cheminformatics and Drug Discovery as well as innovations accelerated using data extracted from it. The review closes by discussing current challenges and possible directions that need to be taken to guarantee that ChEMBL continues to be the pioneering resource for highly curated, open bioactivity data on the European continent and beyond.

Evaluation of selected indigenous spices- and herbs-derived small molecules as potential inhibitors of SREBP and its implications for breast cancer using MD simulations and MMPBSA calculations

In this study, we conducted an extensive analysis of 252 bioactive compounds derived from native spices and herbs for their potential anti-breast cancer activity against sterol regulatory element-binding protein (SREBP), using in silico techniques. To evaluate the oral bioavailability, overall pharmacokinetics, and safety profiles of these compounds, we employed Lipinski's rule of five and ADME descriptors, which depicted 66 lead molecules. These molecules were then docked with the SREBP using molecular docking tools, which revealed that diosgenin and smilagenin were the most promising hits compared to the reference inhibitor betulin, with average binding affinities of - 7.42 and - 7.37 kcal/mol and - 6.27 kcal/mol, respectively. To further assess the stability of these complexes along with betulin, we conducted molecular dynamics simulations over a 100 ns simulation period. We employed various parameters, including the root-mean-square deviation, root-mean-square fluctuation, solvent-accessible surface area, free energy of solvation, and radius of gyration, followed by principal component analysis. Furthermore, we evaluated the toxicity of the selected compounds against various anticancer cell lines, as well as their metabolic activity related to CYP450 metabolism and biological activity spectrum. Based on these results, both molecules exhibited promising drug candidate potential and could be utilized for further experimental analysis to elucidate their anticancer potential.

A novel method for exploration and prediction of the bioactive target of rice bran-derived peptide (KF-8) by integrating computational methods and experiments

The investigation into the bioactive peptide's activity and target action poses a significant challenge in the field of food. An active peptide prepared from rice bran, KF-8, was confirmed to possess antioxidant activity in our previous study, but the specific target was unclear. This study used eight target prediction tools based on artificial intelligence and chemoinformatics to preliminarily screen potential antioxidant targets by integrating different computational methods. Then five different types of docking software were comparatively analyzed to further clarify their interaction sites and possible modes of action. The results showed that SIRT1 and CXCR4 are potential antioxidant targets of KF-8. Different docking software suggested that KF-8 interacts with SIRT1 and CXCR4 as major residues. Meanwhile, the results of Immunofluorescence co-localization experiments showed that the co-localization coefficients of KF-8 with SIRT1 and CXCR4 reached 0.5879 and 0.5684. This study provides new alternative means for the discovery of active peptide targets.

Evaluation of Anti-Inflammatory Activity of the New Cardiotonic Steroid γ-Benzylidene Digoxin 8 (BD-8) in Mice

Cardiotonic steroids are known to bind to Na+/K+-ATPase and regulate several biological processes, including the immune response. The synthetic cardiotonic steroid γ-Benzylidene Digoxin 8 (BD-8) is emerging as a promising immunomodulatory molecule, although it has remained largely unexplored. Therefore, we tested the immunomodulatory potential of BD-8 both in vitro and in vivo. Hence, primary mouse macrophages were incubated with combinations of BD-8 and the pro-inflammatory fungal protein zymosan (ZYM). Nitric oxide (NO) production was determined by Griess reagent and cytokines production was assessed by enzyme-linked immunosorbent assay. Inducible nitric oxide synthase (iNOS), reactive oxygen species (ROS), p-nuclear factor kappa B p65 (NF-κB p65), p-extracellular signal-regulated kinase (p-ERK), and p-p38 were evaluated by flow cytometry. Macrophages exposed to BD-8 displayed reduced phagocytic activity, NO levels, and production of the proinflammatory cytokine IL-1β induced by ZYM. Furthermore, BD-8 diminished the expression of iNOS and phosphorylation of NF-κB p65, ERK, and p38. Additionally, BD-8 exhibited anti-inflammatory capacity in vivo in a carrageenan-induced mouse paw edema model. Taken together, these findings demonstrate the anti-inflammatory activity of BD-8 and further reinforce the potential of cardiotonic steroids and their derivatives as immunomodulatory molecules.

Synthesis, biological evaluation, and molecular docking of novel 1,3,4-substituted-thiadiazole derivatives as potential anticancer agent

In an attempt to develop potent anti-cancer agents, a new 1,3,4-substituted-thiadiazole derivatives (8b-g), starting from 4-substituted-thiazol-2-chloroacetamides (4b-g), were synthesized and evaluated for their cytotoxic effects on multiple human cancer cell lines, including the hepatocellular carcinoma (HEPG-2), human lung carcinoma (A549), human breast carcinoma (MCF-7) and pseudo-normal human embryonic liver (L02) cancer cell lines by an MTT assay. Among all synthesized compounds, compound 8d showed the potent anti-cancer activities with GI50 values of 2.98, 2.85 and 2.53 μM against MCF-7, A549 and HepG-2 cell lines respectively as compared to standard drug Doxorubicin. Furthermore, molecular modelling studies have spotlighted the anchoring role of 1,3,4-substituted-thiadiazole moiety in bonding and hydrophobic interaction with the key amino acid residues. Therefore, these results can provide promising starting points for further development of best anti-cancer agents.

'Computational studies on coumestrol-ArlR interaction to target ArlRS signaling cascade involved in MRSA virulence'

Two component signaling system ArlRS (Autolysis-related locus) regulates adhesion, biofilm formation and virulence in methicillin resistant Staphylococcus aureus. It consists of a histidine kinase ArlS and response regulator ArlR. ArlR is composed of a N-terminal receiver domain and DNA-binding effector domain at C-terminal. ArlR receiver domain dimerizes upon signal recognition and activates DNA binding by effector domain and subsequent virulence expression. In silico simulation and structural data suggest that coumestrol, a phytochemical found in Pueraria montana, forges a strong intermolecular interaction with residues involved in dimer formation and destabilizes ArlR dimerization, an essential conformational switch required for downstream effector domain to bind to virulent loci. Structural and energy profiles of simulated ArlR-coumestrol complexes suggest lower affinity between ArlR monomers due to structural rigidity at the dimer interface hindering the conformational rearrangements relevant for dimer formation. These analyses could be an attractive strategy to develop therapeutics and potent leads molecules response regulators of two component systems in which are involved in MRSA virulence as well as other drug-resistant pathogens.Communicated by Ramaswamy H. Sarma.

Comparative Analysis of the Structure and Pharmacological Properties of Some Piperidines and Host-Guest Complexes of β-Cyclodextrin

Pain and anesthesia are a problem for all physicians. Scientists from different countries are constantly searching for new anesthetic agents and methods of general anesthesia. In anesthesiology, the role and importance of local anesthesia always remain topical. In the present work, a comparative analysis of the results of pharmacological studies on models of the conduction and terminal anesthesia, as well as acute toxicity studies of the inclusion complex of 1-methyl-4-ethynyl-4-hydroxypiperidine (MEP) with β-cyclodextrin, was carried out. A virtual screening and comparative analysis of pharmacological activity were also performed on a number of the prepared piperidine derivatives and their host-guest complexes of β-cyclodextrin to identify the structure-activity relationship. Various programs were used to study biological activity in silico. For comparative analysis of chemical and pharmacological properties, data from previous works were used. For some piperidine derivatives, new dosage forms were prepared as beta-cyclodextrin host-guest complexes. Some compounds were recognized as promising local anesthetics. Pharmacological studies have shown that KFCD-7 is more active than reference drugs in terms of local anesthetic activity and acute toxicity but is less active than host-guest complexes, based on other piperidines. This fact is in good agreement with the predicted results of biological activity.

Ligand-based virtual screening and biological evaluation of inhibitors of Mycobacterium tuberculosis H37Rv

Novel antimycobacterial compounds are needed to expand the existing toolbox of therapeutic agents, which sometimes fail to be effective. In our study we extracted, filtered, and aggregated the diverse data on antimycobacterial activity of chemical compounds from the ChEMBL database version 24.1. These training sets were used to create the classification and regression models with PASS and GUSAR software. The IOC chemical library consisting of approximately 200,000 chemical compounds was screened using these (Q)SAR models to select novel compounds potentially having antimycobacterial activity. The QikProp tool (Schrödinger) was used to predict ADME properties and find compounds with acceptable ADME profiles. As a result, 20 chemical compounds were selected for further biological evaluation, of which 13 were the Schiff bases of isoniazid. To diversify the set of selected compounds we applied substructure filtering and selected an additional 10 compounds, none of which were Schiff bases of isoniazid. Thirty compounds selected using virtual screening were biologically evaluated in a REMA assay against the M. tuberculosis strain H37Rv. Twelve compounds demonstrated MIC below 20 µM (ranging from 2.17 to 16.67 µM) and 18 compounds demonstrated substantially higher MIC values. The discovered antimycobacterial agents represent different chemical classes.

Synthesis of Pyrrolo[3,4-b]pyridin-5-ones via Ugi-Zhu Reaction and In Vitro-In Silico Studies against Breast Carcinoma

An Ugi-Zhu three-component reaction (UZ-3CR) coupled in a one-pot manner to a cascade process (N-acylation/aza Diels-Alder cycloaddition/decarboxylation/dehydration) was performed to synthesize a series of pyrrolo[3,4-b]pyridin-5-ones in 20% to 92% overall yields using ytterbium triflate as a catalyst, toluene as a solvent, and microwaves as a heat source. The synthesized molecules were evaluated in vitro against breast cancer cell lines MDA-MB-231 and MCF-7, finding that compound 1f, at a concentration of 6.25 μM, exhibited a potential cytotoxic effect. Then, to understand the interactions between synthesized compounds and the main proteins related to the cancer cell lines, docking studies were performed on the serine/threonine kinase 1 (AKT1) and Orexetine type 2 receptor (Ox2R), finding moderate to strong binding energies, which matched accurately with the in vitro results. Additionally, molecular dynamics were performed between proteins related to the studied cell lines and the three best ligands.

CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules

Target identification and bioactivity prediction are critical steps in the drug discovery process. Here we introduce CODD-Pred (COmprehensive Drug Design Predictor), an online web server with well-curated data sets from the GOSTAR database, which is designed with a dual purpose of predicting potential protein drug targets and computing bioactivity values of small molecules. We first designed a double molecular graph perception (DMGP) framework for target prediction based on a large library of 646 498 small molecules interacting with 640 human targets. The framework achieved a top-5 accuracy of over 80% for hitting at least one target on both external validation sets. Additionally, its performance on the external validation set comprising 200 molecules surpassed that of four existing target prediction servers. Second, we collected 56 targets closely related to the occurrence and development of cancer, metabolic diseases, and inflammatory immune diseases and developed a multi-model self-validation activity prediction (MSAP) framework that enables accurate bioactivity quantification predictions for small-molecule ligands of these 56 targets. CODD-Pred is a handy tool for rapid evaluation and optimization of small molecules with specific target activity. CODD-Pred is freely accessible at http://codd.iddd.group/.

Reverse Screening of Boronic Acid Derivatives: Analysis of Potential Antiproliferative Effects on a Triple-Negative Breast Cancer Model In Vitro

It has been demonstrated that different organoboron compounds interact with some well-known molecular targets, including serine proteases, transcription factors, receptors, and other important molecules. Several approaches to finding the possible beneficial effects of boronic compounds include various in silico tools. This work aimed to find the most probable targets for five aromatic boronic acid derivatives. In silico servers, SuperPred, PASS-Targets, and Polypharmacology browser 2 (PPB2) suggested that the analyzed compounds have anticancer properties. Based on these results, the antiproliferative effect was evaluated using an in vitro model of triple-negative breast cancer (4T1 cells in culture). It was demonstrated that phenanthren-9-yl boronic acid and 6-hydroxynaphthalen-2-yl boronic acid have cytotoxic properties at sub-micromolar concentrations. In conclusion, using in silico approaches and in vitro analysis, we found two boronic acid derivatives with potential anticancer activity.

"Identification of alkaloid compounds as potent inhibitors of Mycobacterium tuberculosis NadD using computational strategies"

Mycobacterium tuberculosis is leading cause of death worldwide. NAD participates in a host of redox reactions in energy landscape of organisms. Several studies implicate surrogate energy pathways involving NAD pools as important in survival of active as well as dormant mycobacteria. One of the NAD metabolic pathway enzyme, nicotinate mononucleotide adenylyltransferase (NadD) is indispensable in mycobacterial NAD metabolism and is perceived as an attractive drug target in pathogen. In this study, we have employed in silico screening, simulation and MM-PBSA strategies to identify potentially important alkaloid compounds against mycobacterial NadD for structure-based inhibitor development. We have performed an exhaustive structure-based virtual screening of an alkaloid library, ADMET, DFT profiling followed by Molecular Dynamics (MD) simulation, and Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculation to identify 10 compounds which exhibit favourable drug like properties and interactions. Interaction energies of these 10 alkaloid molecules range between -190 kJ/mol and -250 kJ/mol. These compounds could be promising starting point in the development of selective inhibitors against Mycobacterium tuberculosis.

Rohitukine content across the geographical distribution of Dysoxylum binectariferum Hook F. and its natural derivatives as potential sources of CDK inhibitors

Dysoxylum binectariferum is an important medicinal plant distributed in the Western Ghats of India. The species has gained international importance for its anticancer component, rohitukine, a chromone alkaloid. Flavopiridol, P-276-00 and IIIM-290 are the derivatives of rohitukine in clinical trials against a wide range of cancers. Flavopiridol was recently approved as an orphan drug for chronic lymphocytic leukemia treatment. In this study, we report the isolation and characterization of rohitukine from the bark of D. binectariferum. Further, rohitukine was estimated across the Western-Ghats and the North-East regions of India. Additionally, D. binectariferum is also reported (∼45 compounds) to produce many natural derivatives of rohitukine and terpenoids, which were investigated in-silico to reveal promising CDK inhibitors. The metabolite fingerprinting of tissues of D. binectariferum was studied using HPTLC and FTIR. The distribution of major chromone alkaloid rohitukine was estimated by HPLC. Further, the pharmacological potential of D. binectariferum compounds was evaluated in-silico by discovering the potential protein targets, molecular docking, ADMET analysis and MD simulation. The isolation of rohitukine has yielded 0.6% from the bark of D. binectariferum. A higher percent of rohitukine was found in the Jog populations (0.58% & 1.28%: leaf & bark), whereas least was observed in the Phasighat population (∼0.06%: both leaf & bark). Across the geographic regions, a higher percent of rohitukine was found in the Central-southern Western Ghats, whereas lower in the northern parts of the Western Ghats and Northeast regions. The leaves produce a considerably higher percent of rohitukine and could be used as a sustainable source of rohitukine. The rohitukine analogues, along with other chromone alkaloids of D. binecatariferum were found to be more interactive with the "kinases" family of proteins, majorly "Serine/threonine-protein kinase PFTAIRE-2" (CDK15) with high confidence level (0.94-0.98). The molecular docking of these chromone alkaloids found a strong binding energy with six CDKs (-3.1 to -10.6 kcal/mol) along with a promising ADMET profile. In addition, molecular dynamic simulation found that the rohitukine complexes are virtually constant with CDK-1, 2, 9 and 15, which is substantiated with MM-PBSA free energy calculations. The chromone alkaloids, majorly rohitukine and its analogues were closely clustered with flavopiridol, P-276-00 and IIIM-290 along with other chrotacumines in the chemical phylogeny. In conclusion, D. binectariferum is a rich source of chromone alkaloids, which could lead to the discovery of more potential scaffolding for CDK inhibitors as anticancer drugs.

Anticancer Activities of 9-chloro-6-(piperazin-1-yl)-11H-indeno[1,2-c] quinolin-11-one (SJ10) in Glioblastoma Multiforme (GBM) Chemoradioresistant Cell Cycle-Related Oncogenic Signatures

Simple Summary

Glioblastoma multiforme (GBM) remains to be the most frequent malignant tumor of the central nervous system (CNS), which accounts for approximately 54% of all primary brain gliomas. Current treatment modalities for GBM include surgical resection, followed by radiotherapy and chemotherapy with temozolomide (TMZ). However, due to its genetic heterogeneity, GBM tumors always recur, due to treatment reasistance. The aim of this study was to identify molecular gene signatures, responsible for cancer initiation, progression, resistances and to treatment, metastasis, and also evaluate the potency of our novel compounds SJ10 as potential target for CCNB1/CDC42/MAPK7/CD44 oncogenic signatures. Accordingly, we used computational simulation and identify these signatures as regulators of the cell cycle in GBM, which leads to cancer development and metastasis. We also showed the antiproliferative and cytotoxic effects of SJ10 compound against a panel of NCI-60 cancer cell lines. This suggests the potential of the compounds to inhibit CCNB1/CDC42/MAPK7/CD44 in GBM.

Abstract

Current anticancer treatments are inefficient against glioblastoma multiforme (GBM), which remains one of the most aggressive and lethal cancers. Evidence has shown the presence of glioblastoma stem cells (GSCs), which are chemoradioresistant and associated with high invasive capabilities in normal brain tissues. Moreover, accumulating studies have indicated that radiotherapy contributes to abnormalities in cell cycle checkpoints, including the G₁/S and S phases, which may potentially lead to resistance to radiation. Through computational simulations using bioinformatics, we identified several GBM oncogenes that are involved in regulating the cell cycle. Cyclin B1 (CCNB1) is one of the cell cycle-related genes that was found to be upregulated in GBM. Overexpression of CCNB1 was demonstrated to be associated with higher grades, proliferation, and metastasis of GBM. Additionally, increased expression levels of CCNB1 were reported to regulate activation of mitogen-activated protein kinase 7 (MAPK7) in the G₂/M phase, which consequently modulates mitosis; additionally, in clinical settings, MAPK7 was demonstrated to promote resistance to temozolomide (TMZ) and poor patient survival. Therefore, MAPK7 is a potential novel drug target due to its dysregulation and association with TMZ resistance in GBM. Herein, we identified MAPK7/extracellular regulated kinase 5 (ERK5) genes as being overexpressed in GBM tumors compared to normal tissues. Moreover, our analysis revealed increased levels of the cell division control protein homolog (CDC42), a protein which is also involved in regulating the cell cycle through the G₁ phase in GBM tissues. This therefore suggests crosstalk among CCNB1/CDC42/MAPK7/cluster of differentiation 44 (CD44) oncogenic signatures in GBM through the cell cycle. We further evaluated a newly synthesized small molecule, SJ10, as a potential target agent of the CCNB1/CDC42/MAPK7/CD44 genes through target prediction tools and found that SJ10 was indeed a target compound for the above-mentioned genes; in addition, it displayed inhibitory activities against these oncogenes as observed from molecular docking analysis.

Comprehensive Omics Analysis of a Novel Small-Molecule Inhibitor of Chemoresistant Oncogenic Signatures in Colorectal Cancer Cell with Antitumor Effects

Tumor recurrence from cancer stem cells (CSCs) and metastasis often occur post-treatment in colorectal cancer (CRC), leading to chemoresistance and resistance to targeted therapy. MYC is a transcription factor in the nuclei that modulates cell growth and development, and regulates immune response in an antitumor direction by mediating programmed death ligand 1 (PD-L1) and promoting CRC tumor recurrence after adjuvant chemotherapy. However, the molecular mechanism through which c-MYC maintains stemness and confers treatment resistance still remains elusive in CRC. In addition, recent reports demonstrated that CRC solid colon tumors expresses C-X-C motif chemokine ligand 8 (CXCL8). Expression of CXCL8 in CRC was reported to activate the expression of PD-L1 immune checkpoint through c-MYC, this ultimately induces chemoresistance in CRC. Accumulating studies have also demonstrated increased expression of CXCL8, matrix metalloproteinase 7 (MMP7), tissue inhibitor of metalloproteinase 1 (TIMP1), and epithelial-to-mesenchymal transition (EMT) components, in CRC tumors suggesting their potential collaboration to promote EMT and CSCs. TIMP1 is MMP-independent and regulates cell development and apoptosis in various cancer cell types, including CRC. Recent studies showed that TIMP1 cleaves CXCL8 on its chemoattractant, thereby influencing its mechanistic response to therapy. This therefore suggests crosstalk among the c-MYC/CXCL8/TIMP1 oncogenic signatures. In this study, we explored computer simulations through bioinformatics to identify and validate that the MYC/CXCL8/TIMP1 oncogenic signatures are overexpressed in CRC, Moreover, our docking results exhibited putative binding affinities of the above-mentioned oncogenes, with our novel small molecule, RV59, Finally, we demonstrated the anticancer activities of RV59 against NCI human CRC cancer cell lines both as single-dose and dose-dependent treatments, and also demonstrated the MYC/CXCL8/TIMP1 signaling pathway as a potential RV59 drug target.

Structurally diverse diterpenoid alkaloids from the lateral roots of Aconitum carmichaelii Debx. and their anti-tumor activities based on in vitro systematic evaluation and network pharmacology analysis

Thirty-seven diterpenoid alkaloids (DAs) with diverse structures were isolated and identified from the lateral roots of Aconitum carmichaelii Debx., comprising eight C20-DAs and twenty-nine C19-DAs. Besides the 31 known DAs identified by comparing the 1H NMR and 13C NMR data with those reported in the literature, the structures of four new compounds (1, 14, 17, and 25), and two other compounds (26 and 37) which were reported to be synthesized previously, were also elucidated based on the comprehensive analysis of their HR-ESI-MS, 1D and 2D NMR spectra, including 1H-1H COSY, HSQC and HMBC and NOESY/ROESY. Among them, compound 1 represents the first example of a C20-DA glucoside. Besides, the anti-tumor activities of all the isolated compounds against human non-small-cell lung cancer A549 and H460 cells were systematically evaluated by MTT methods. The results revealed that all of the C19-DAs possessed moderate activities against both of the two cell lines with IC50 values ranging from 7.97 to 28.42 μM, and their structure-activity relationships indicated the active sites of C-8, C-10, and C-14 positions and the nitrogen atom in the C19-DA skeleton. In addition, all of the isolated DAs, with chemical structures confirmed, were further applied for network pharmacology analysis, in order to give an insight into the possible mechanisms of their anti-tumor activities. As a result, 173 potential targets and three most important pathways related to non-small-cell lung carcinoma were finally unearthed.

Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods

Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity. These efforts generated the largest publicly available such data set comprising > 80,000 compounds measured against a total of 59 acute systemic toxicity end points. This data was used for developing multiple single- and multitask models utilizing random forest, deep neural networks, convolutional, and graph convolutional neural network approaches. For the first time, we also reported the consensus models based on different multitask approaches. To the best of our knowledge, prediction models for 36 of the 59 end points have never been published before. Furthermore, our results demonstrated a significantly better performance of the consensus model obtained from three multitask learning approaches that particularly predicted the 29 smaller tasks (less than 300 compounds) better than other models developed in the study. The curated data set and the developed models have been made publicly available at https://github.com/ncats/ld50-multitask, https://predictor.ncats.io/, and https://cactus.nci.nih.gov/download/acute-toxicity-db (data set only) to support regulatory and research applications.

Prediction of Protein-ligand Interaction Based on Sequence Similarity and Ligand Structural Features

Computationally predicting the interaction of proteins and ligands presents three main directions: the search of new target proteins for ligands, the search of new ligands for targets, and predicting the interaction of new proteins and new ligands. We proposed an approach providing the fuzzy classification of protein sequences based on the ligand structural features to analyze the latter most complicated case. We tested our approach on five protein groups, which represented promised targets for drug-like ligands and differed in functional peculiarities. The training sets were built with the original procedure overcoming the data ambiguity. Our study showed the effective prediction of new targets for ligands with an average accuracy of 0.96. The prediction of new ligands for targets displayed the average accuracy 0.95; accuracy estimates were close to our previous results, comparable in accuracy to those of other methods or exceeded them. Using the fuzzy coefficients reflecting the target-to-ligand specificity, we provided predicting interactions for new proteins and new ligands; the obtained accuracy values from 0.89 to 0.99 were acceptable for such a sophisticated task. The protein kinase family case demonstrated the ability to account for subtle features of proteins and ligands required for the specificity of protein–ligand interaction.

[Computer-aided drug design: from discovery of novel pharmaceutical agents to systems pharmacology]

New drug discovery is based on the analysis of public information about the mechanisms of the disease, molecular targets, and ligands, which interaction with the target could lead to the normalization of the pathological process. The available data on diseases, drugs, pharmacological effects, molecular targets, and drug-like substances, taking into account the combinatorics of the associative relations between them, correspond to the Big Data. To analyze such data, the application of computer-aided drug design methods is necessary. An overview of the studies in this area performed by the Laboratory for Structure-Function Based Drug Design of IBMC is presented. We have developed the approaches to identifying promising pharmacological targets, predicting several thousand types of biological activity based on the structural formula of the compound, analyzing protein-ligand interactions based on assessing local similarity of amino acid sequences, identifying likely molecular mechanisms of side effects of drugs, calculating the integral toxicity of drugs taking into account their metabolism, have been developed in the human body, predicting sustainable and sensitive options strains and evaluating the effectiveness of combinations of antiretroviral drugs in patients, taking into account the molecular genetic characteristics of the clinical isolates of HIV-1. Our computer programs are implemented as the web-services freely available on the Internet, which are used by thousands of researchers from many countries of the world to select the most promising substances for the synthesis and determine the priority areas for experimental testing of their biological activity.

Prediction of Protein-Ligand Interaction Based on the Positional Similarity Scores Derived from Amino Acid Sequences

The affinity of different drug-like ligands to multiple protein targets reflects general chemical–biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein–ligand interactions based on pairwise sequence alignment provides reasonable accuracy if the ligands’ specificity well coincides with the phylogenic taxonomy of the proteins. Methods using multiple alignment require an accurate match of functionally significant residues. Such conditions may not be met in the case of diverged protein families. To overcome these limitations, we propose an approach based on the analysis of local sequence similarity within the set of analyzed proteins. The positional scores, calculated by sequence fragment comparisons, are used as input data for the Bayesian classifier. Our approach provides a prediction accuracy comparable or exceeding those of other methods. It was demonstrated on the popular Gold Standard test sets, presenting different sequence heterogeneity and varying from the group, including different protein families to the more specific groups. A reasonable prediction accuracy was also found for protein kinases, displaying weak relationships between sequence phylogeny and inhibitor specificity. Thus, our method can be applied to the broad area of protein–ligand interactions.

AntiBac-Pred: A Web Application for Predicting Antibacterial Activity of Chemical Compounds

Discovery of new antibacterial agents is a never-ending task of medicinal chemistry. Every new drug brings significant improvement to patients with bacterial infections, but prolonged usage of antibacterials leads to the emergence of resistant strains. Therefore, novel active structures with new modes of action are required. We describe a web application called AntiBac-Pred aimed to help users in the rational selection of the chemical compounds for experimental studies of antibacterial activity. This application is developed using antibacterial activity data available in ChEMBL and PASS software. It allows users to classify chemical structures of interest into growth inhibitors or noninhibitors of 353 different bacteria strains, including both resistant and nonresistant ones.

Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models

Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here, we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationship (QSAR) models. We propose a new deep learning consensus architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models. This method improves knowledge transfer across different target/assays while also integrating contributions from models based on different descriptors. The proposed approach was validated and compared with proteochemometrics, multitask deep learning, and Random Forest methods paired with various descriptors types. DLCA models demonstrated improved prediction accuracy for both regression and classification tasks. The best models together with their modeling sets are provided through publicly available web services at https://predictor.ncats.io .

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.

PASS-based prediction of metabolites detection in biological systems

Metabolite identification is an essential part of the drug discovery and development process. Experimental methods allow identifying metabolites and estimating their relative amount, but they require cost-intensive and time-consuming techniques. Computational methods for metabolite prediction are devoid of these shortcomings and may be applied at the early stage of drug discovery. In this study, we investigated the possibility of creating SAR models for the prediction of the qualitative metabolite yield ('major', 'minor', "trace" and "negligible") depending on species and biological experimental systems. In addition, we have created models for prediction of xenobiotic excretion depending on its administration route for different species. The prediction is based on an algorithm of naïve Bayes classifier implemented in PASS software. The average accuracy of prediction was 0.91 for qualitative metabolite yield prediction and 0.89 for prediction of xenobiotic excretion. The created models were included as a component of MetaTox web application, which allows predicting the xenobiotic metabolism pathways ( http://www.way2drug.com/mg ).

Assessment of the cardiovascular adverse effects of drug-drug interactions through a combined analysis of spontaneous reports and predicted drug-target interactions

Adverse drug effects (ADEs) are one of the leading causes of death in developed countries and are the main reason for drug recalls from the market, whereas the ADEs that are associated with action on the cardiovascular system are the most dangerous and widespread. The treatment of human diseases often requires the intake of several drugs, which can lead to undesirable drug-drug interactions (DDIs), thus causing an increase in the frequency and severity of ADEs. An evaluation of DDI-induced ADEs is a nontrivial task and requires numerous experimental and clinical studies. Therefore, we developed a computational approach to assess the cardiovascular ADEs of DDIs. This approach is based on the combined analysis of spontaneous reports (SRs) and predicted drug-target interactions to estimate the five cardiovascular ADEs that are induced by DDIs, namely, myocardial infarction, ischemic stroke, ventricular tachycardia, cardiac failure, and arterial hypertension. We applied a method based on least absolute shrinkage and selection operator (LASSO) logistic regression to SRs for the identification of interacting pairs of drugs causing corresponding ADEs, as well as noninteracting pairs of drugs. As a result, five datasets containing, on average, 3100 potentially ADE-causing and non-ADE-causing drug pairs were created. The obtained data, along with information on the interaction of drugs with 1553 human targets predicted by PASS Targets software, were used to create five classification models using the Random Forest method. The average area under the ROC curve of the obtained models, sensitivity, specificity and balanced accuracy were 0.837, 0.764, 0.754 and 0.759, respectively. The predicted drug targets were also used to hypothesize the potential mechanisms of DDI-induced ventricular tachycardia for the top-scoring drug pairs. The created five classification models can be used for the identification of drug combinations that are potentially the most or least dangerous for the cardiovascular system.

Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space

Acute toxicity is one of the most challenging properties to predict purely with computational methods due to its direct relationship to biological interactions. Moreover, toxicity can be represented by different end points: it can be measured for different species using different types of administration, etc., and it is questionable if the knowledge transfer between end points is possible. We performed a comparative study of prediction multitask toxicity for a broad chemical space using different descriptors and modeling algorithms and applied multitask learning for a large toxicity data set extracted from the Registry of Toxic Effects of Chemical Substances (RTECS). We demonstrated that multitask modeling provides significant improvement over single-output models and other machine learning methods. Our research reveals that multitask learning can be very useful to improve the quality of acute toxicity modeling and raises a discussion about the usage of multitask approaches for regulation purposes. Our MultiTox models are freely available in OCHEM platform ( ochem.eu/multitox ) under CC-BY-NC license.

In Silico Drug-Target Profiling

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

Here we report PPB2 as a target prediction tool assigning targets to a query molecule based on ChEMBL data. PPB2 computes ligand similarities using molecular fingerprints encoding composition (MQN), molecular shape and pharmacophores (Xfp), and substructures (ECfp4) and features an unprecedented combination of nearest neighbor (NN) searches and Naı̈ve Bayes (NB) machine learning, together with simple NN searches, NB and Deep Neural Network (DNN) machine learning models as further options. Although NN(ECfp4) gives the best results in terms of recall in a 10-fold cross-validation study, combining NN searches with NB machine learning provides superior precision statistics, as well as better results in a case study predicting off-targets of a recently reported TRPV6 calcium channel inhibitor, illustrating the value of this combined approach. PPB2 is available to assess possible off-targets of small molecule drug-like compounds by public access at http://gdb.unibe.ch .

Artificial Intelligence in Drug Design

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.

Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point

Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.

How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors

Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of "active" and "inactive" compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.

Identification of potential drug targets for treatment of refractory epilepsy using network pharmacology

Epilepsy is the fourth most common neurological disease after migraine, stroke, and Alzheimer's disease. Approximately one-third of all epilepsy cases are refractory to the existing anticonvulsants. Thus, there is an unmet need for newer antiepileptic drugs (AEDs) to manage refractory epilepsy (RE). Discovery of novel AEDs for the treatment of RE further retards for want of potential pharmacological targets, unavailable due to unclear etiology of this disease. In this regard, network pharmacology as an area of bioinformatics is gaining popularity. It combines the methods of network biology and polypharmacology, which makes it a promising approach for finding new molecular targets. This work is aimed at discovering new pharmacological targets for the treatment of RE using network pharmacology methods. In the framework of our study, the genes associated with the development of RE were selected based on analysis of available data. The methods of network pharmacology were used to select 83 potential pharmacological targets linked to the selected genes. Then, 10 most promising targets were chosen based on analysis of published data. All selected target proteins participate in biological processes, which are considered to play a key role in the development of RE. For 9 of 10 selected targets, the potential associations with different kinds of epilepsy have been recently mentioned in the literature published, which gives additional evidence that the approach applied is rather promising.

Computer-aided prediction of biological activity spectra for chemical compounds: opportunities and limitation

An essential characteristic of chemical compounds is their biological activity since its presence can become the basis for the use of the substance for therapeutic purposes, or, on the contrary, limit the possibilities of its practical application due to the manifestation of side action and toxic effects. Computer assessment of the biological activity spectra makes it possible to determine the most promising directions for the study of the pharmacological action of particular substances, and to filter out potentially dangerous molecules at the early stages of research. For more than 25 years, we have been developing and improving the computer program PASS (Prediction of Activity Spectra for Substances), designed to predict the biological activity spectrum of substance based on the structural formula of its molecules. The prediction is carried out by the analysis of structure-activity relationships for the training set, which currently contains information on structures and known biological activities for more than one million molecules. The structure of the organic compound is represented in PASS using Multilevel Neighborhoods of Atoms descriptors; the activity prediction for new compounds is performed by the naive Bayes classifier and the structure-activity relationships determined by the analysis of the training set. We have created and improved both local versions of the PASS program and freely available web resources based on PASS (http://www.way2drug.com). They predict several thousand biological activities (pharmacological effects, molecular mechanisms of action, specific toxicity and adverse effects, interaction with the unwanted targets, metabolism and action on molecular transport), cytotoxicity for tumor and non-tumor cell lines, carcinogenicity, induced changes of gene expression profiles, metabolic sites of the major enzymes of the first and second phases of xenobiotics biotransformation, and belonging to substrates and/or metabolites of metabolic enzymes. The web resource Way2Drug is used by over 18,000 researchers from more than 90 countries around the world, which allowed them to obtain over 600,000 predictions and publish about 500 papers describing the obtained results. The analysis of the published works shows that in some cases the interpretation of the prediction results presented by the authors of these publications requires an adjustment. In this work, we provide the theoretical basis and consider, on particular examples, the opportunities and limitations of computer-aided prediction of biological activity spectra.

Prediction of metabolites of epoxidation reaction in MetaTox

Biotransformation is a process of the chemical modifications which may lead to the reactive metabolites, in particular the epoxides. Epoxide reactive metabolites may cause the toxic effects. The prediction of such metabolites is important for drug development and ecotoxicology studies. Epoxides are formed by some oxidation reactions, usually catalysed by cytochromes P450, and represent a large class of three-membered cyclic ethers. Identification of molecules, which may be epoxidized, and indication of the specific location of epoxide functional group (which is called SOE - site of epoxidation) are important for prediction of epoxide metabolites. Datasets from 355 molecules and 615 reactions were created for training and validation. The prediction of SOE is based on a combination of LMNA (Labelled Multilevel Neighbourhood of Atom) descriptors and Bayesian-like algorithm implemented in PASS software and MetaTox web-service. The average invariant accuracy of prediction (AUC) calculated in leave-one-out and 20-fold cross-validation procedures is 0.9. Prediction of epoxide formation based on the created SAR model is included as the component of MetaTox web-service ( http://www.way2drug.com/mg ).

How good are publicly available web services that predict bioactivity profiles for drug repurposing?

Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.

In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions

Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research.

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.