Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks

Modeling of Effectiveness of N3-Substituted Amidrazone Derivatives as Potential Agents against Gram-Positive Bacteria

Prediction of the antibacterial activity of new chemical compounds is an important task, due to the growing problem of bacterial drug resistance. Generalized linear models (GLMs) were created using 85 amidrazone derivatives based on the results of antimicrobial activity tests, determined as the minimum inhibitory concentration (MIC) against Gram-positive bacteria: Staphylococcus aureus, Enterococcus faecalis, Micrococcus luteus, Nocardia corallina, and Mycobacterium smegmatis. For the analysis of compounds characterized by experimentally measured MIC values, we included physicochemical properties (e.g., molecular weight, number of hydrogen donors and acceptors, topological polar surface area, compound percentages of carbon, nitrogen, and oxygen, melting points, and lipophilicity) as potential predictors. The presence of R1 and R2 substituents, as well as interactions between melting temperature and R1 or R2 substituents, were also considered. The set of potential predictors also included possible biological effects (e.g., antibacterial, antituberculotic) of tested compounds calculated with the PASS (Prediction of Activity Spectra for Substances) program. Using GLMs with least absolute shrinkage and selection (LASSO), least-angle regression, and stepwise selection, statistically significant models with the optimal value of the adjusted determination coefficient and of seven fit criteria were chosen, e.g., Akaike's information criterion. The most often selected variables were as follows: molecular weight, PASS_antieczematic, PASS_anti-inflam, squared melting temperature, PASS_antitumor, and experimental lipophilicity. Additionally, relevant to the bacterial strain, the interactions between melting temperature and R1 or R2 substituents were selected, indicating that the relationship between MIC and melting temperature depends on the type of R1 or R2 substituent.

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood-brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.

Identification of Riluzole derivatives as novel calmodulin inhibitors with neuroprotective activity by a joint synthesis, biosensor, and computational guided strategy

The development of new molecules for the treatment of calmodulin related cardiovascular or neurodegenerative diseases is an interesting goal. In this work, we introduce a novel strategy with four main steps: (1) chemical synthesis of target molecules, (2) Förster Resonance Energy Transfer (FRET) biosensor development and in vitro biological assay of new derivatives, (3) Cheminformatics models development and in vivo activity prediction, and (4) Docking studies. This strategy is illustrated with a case study. Firstly, a series of 4-substituted Riluzole derivatives 1-3 were synthetized through a strategy that involves the construction of the 4-bromoriluzole framework and its further functionalization via palladium catalysis or organolithium chemistry. Next, a FRET biosensor for monitoring Ca2+-dependent CaM-ligands interactions has been developed and used for the in vitro assay of Riluzole derivatives. In particular, the best inhibition (80%) was observed for 4-methoxyphenylriluzole 2b. Besides, we trained and validated a new Networks Invariant, Information Fusion, Perturbation Theory, and Machine Learning (NIFPTML) model for predicting probability profiles of in vivo biological activity parameters in different regions of the brain. Next, we used this model to predict the in vivo activity of the compounds experimentally studied in vitro. Last, docking study conducted on Riluzole and its derivatives has provided valuable insights into their binding conformations with the target protein, involving calmodulin and the SK4 channel. This new combined strategy may be useful to reduce assay costs (animals, materials, time, and human resources) in the drug discovery process of calmodulin inhibitors.

Interdisciplinary-Inspired Smart Antibacterial Materials and Their Biomedical Applications

The discovery of antibiotics has saved millions of lives, but the emergence of antibiotic-resistant bacteria has become another problem in modern medicine. To avoid or reduce the overuse of antibiotics in antibacterial treatments, stimuli-responsive materials, pathogen-targeting nanoparticles, immunogenic nano-toxoids, and biomimetic materials are being developed to make sterilization better and smarter than conventional therapies. The common goal of smart antibacterial materials (SAMs) is to increase the antibiotic efficacy or function via an antibacterial mechanism different from that of antibiotics in order to increase the antibacterial and biological properties while reducing the risk of drug resistance. The research and development of SAMs are increasingly interdisciplinary because new designs require the knowledge of different fields and input/collaboration from scientists in different fields. A good understanding of energy conversion in materials, physiological characteristics in cells and bacteria, and bactericidal structures and components in nature are expected to promote the development of SAMs. In this review, the importance of multidisciplinary insights for SAMs is emphasized, and the latest advances in SAMs are categorized and discussed according to the pertinent disciplines including materials science, physiology, and biomimicry.

Artifical intelligence: a virtual chemist for natural product drug discovery

Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.

Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature

INTRODUCTION

Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations.

AREAS COVERED

The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process.

EXPERT OPINION

Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.

Chimaphila umbellata; a biotechnological perspective on the coming-of-age prince's pine

Chimaphila umbellata has been studied for almost two centuries now, with the first paper exploring the phytochemistry of the plant published in 1860. Almost all contemporary studies focus on the biotechnological advances of C. umbellata including its utilization as a natural alternative in the cosmetic, food, biofuel, and healthcare industry, with a special focus on its therapeutic uses. This literature review critically investigates the significance and applications of secondary metabolites extracted from the plant and presses on the biotechnological approaches to improve its utilization. C. umbellata is home to many industrially and medicinally important phytochemicals, the majority of which belong to phenolics, sterols, and triterpenoids. Other important compounds include 5-hydroxymethylfurfural, isohomoarbutin, and methyl salicylate (the only essential oil of the plant). Chimaphilin is the characteristic phytochemical of the plant. This review focuses on the phytochemistry of C. umbellata and digs into their chemical structures and attributes. It further discusses the challenges of working with C. umbellata including its alarming conservation status, problems with in-vitro cultivation, and research and development issues. This review concludes with recommendations based on biotechnology, bioinformatics, and their crucial interface.

Photo-Antibacterial Activity of Two-Dimensional (2D)-Based Hybrid Materials: Effective Treatment Strategy for Controlling Bacterial Infection

Bacterial contamination in water bodies is a severe scourge that affects human health and causes mortality and morbidity. Researchers continue to develop next-generation materials for controlling bacterial infections from water. Photo-antibacterial activity continues to gain the interest of researchers due to its adequate, rapid, and antibiotic-free process. Photo-antibacterial materials do not have any side effects and have a minimal chance of developing bacterial resistance due to their rapid efficacy. Photocatalytic two-dimensional nanomaterials (2D-NMs) have great potential for the control of bacterial infection due to their exceptional properties, such as high surface area, tunable band gap, specific structure, and tunable surface functional groups. Moreover, the optical and electric properties of 2D-NMs might be tuned by creating heterojunctions or by the doping of metals/carbon/polymers, subsequently enhancing their photo-antibacterial ability. This review article focuses on the synthesis of 2D-NM-based hybrid materials, the effect of dopants in 2D-NMs, and their photo-antibacterial application. We also discuss how we could improve photo-antibacterials by using different strategies and the role of artificial intelligence (AI) in the photocatalyst and in the degradation of pollutants. Finally, we discuss was of improving the photo-antibacterial activity of 2D-NMs, the toxicity mechanism, and their challenges.

Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles

Respiratory viruses are infectious agents, which can cause pandemics. Although nowadays the danger associated with respiratory viruses continues to be evidenced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the virus responsible for the current COVID-19 pandemic, other viruses such as SARS-CoV-1, the influenza A and B viruses (IAV and IBV, respectively), and the respiratory syncytial virus (RSV) can lead to globally spread viral diseases. Also, from a biological point of view, most of these viruses can cause an organ-damaging hyperinflammatory response known as the cytokine storm (CS). Computational approaches constitute an essential component of modern drug development campaigns, and therefore, they have the potential to accelerate the discovery of chemicals able to simultaneously inhibit multiple molecular and nonmolecular targets. We report here the first multicondition model based on quantitative structure-activity relationships and an artificial neural network (mtc-QSAR-ANN) for the virtual design and prediction of molecules with dual pan-antiviral and anti-CS profiles. Our mtc-QSAR-ANN model exhibited an accuracy higher than 80%. By interpreting the different descriptors present in the mtc-QSAR-ANN model, we could retrieve several molecular fragments whose assembly led to new molecules with drug-like properties and predicted pan-antiviral and anti-CS activities.

Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2-Acylpyrrole Derivatives

In this work, the SOFT.PTML tool has been used to pre-process a ChEMBL dataset of pre-clinical assays of antileishmanial compound candidates. A comparative study of different ML algorithms, such as logistic regression (LOGR), support vector machine (SVM), and random forests (RF), has shown that the IFPTML-LOGR model presents excellent values of specificity and sensitivity (81-98%) in training and validation series. The use of this software has been illustrated with a practical case study focused on a series of 28 derivatives of 2-acylpyrroles 5a,b, obtained through a Pd(II)-catalyzed C-H radical acylation of pyrroles. Their in vitro leishmanicidal activity against visceral (L. donovani) and cutaneous (L. amazonensis) leishmaniasis was evaluated finding that compounds 5bc (IC₅₀ = 30.87 μM, SI > 10.17) and 5bd (IC₅₀ = 16.87 μM, SI > 10.67) were approximately 6-fold more selective than the drug of reference (miltefosine) in in vitro assays against L. amazonensis promastigotes. In addition, most of the compounds showed low cytotoxicity, CC₅₀ > 100 μg/mL in J774 cells. Interestingly, the IFPMTL-LOGR model predicts correctly the relative biological activity of these series of acylpyrroles. A computational high-throughput screening (cHTS) study of 2-acylpyrroles 5a,b has been performed calculating >20,700 activity scores vs a large space of 647 assays involving multiple Leishmania species, cell lines, and potential target proteins. Overall, the study demonstrates that the SOFT.PTML all-in-one strategy is useful to obtain IFPTML models in a friendly interface making the work easier and faster than before. The present work also points to 2-acylpyrroles as new lead compounds worthy of further optimization as antileishmanial hits.

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.

Machine Learning in Antibacterial Drug Design

Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.

Biological Membrane-Penetrating Peptides: Computational Prediction and Applications

Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides’ penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.

PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors

Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwide and exhibits a remarkably poor prognosis. To date, discovering anti-PANC agents remains a very complex and expensive process. Computational approaches can accelerate the search for anti-PANC agents. We report for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules that can simultaneously inhibit multiple PANC cell lines and PANC-related proteins, such as caspase-1, tumor necrosis factor-alpha (TNF-alpha), and the insulin-like growth factor 1 receptor (IGF1R). Both PTML-MLP models exhibited accuracies higher than 78%. Using the interpretation from one of the PTML-MLP models as a guideline, we extracted different molecular fragments desirable for the inhibition of the PANC cell lines and the aforementioned PANC-related proteins and then assembled some of those fragments to form three new molecules. The two PTML-MLP models predicted the designed molecules as potentially versatile anti-PANC agents through inhibition of the three PANC-related proteins and multiple PANC cell lines. Conclusions: This work opens new horizons for the application of the PTML modeling methodology to anticancer research.

In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

Inflammation involves a complex biological response of the body tissues to damaging stimuli. When dysregulated, inflammation led by biomolecular mediators such as caspase-1 and tumor necrosis factor-alpha (TNF-alpha) can play a detrimental role in the progression of different medical conditions such as cancer, neurological disorders, autoimmune diseases, and cytokine storms caused by viral infections such as COVID-19. Computational approaches can accelerate the search for dual-target drugs able to simultaneously inhibit the aforementioned proteins, enabling the discovery of wide-spectrum anti-inflammatory agents. This work reports the first multicondition model based on quantitative structure–activity relationships and a multilayer perceptron neural network (mtc-QSAR-MLP) for the virtual screening of agency-regulated chemicals as versatile anti-inflammatory therapeutics. The mtc-QSAR-MLP model displayed accuracy higher than 88%, and was interpreted from a physicochemical and structural point of view. When using the mtc-QSAR-MLP model as a virtual screening tool, we could identify several agency-regulated chemicals as dual inhibitors of caspase-1 and TNF-alpha, and the experimental information later retrieved from the scientific literature converged with our computational results. This study supports the capabilities of our mtc-QSAR-MLP model in anti-inflammatory therapy with direct applications to current health issues such as the COVID-19 pandemic.

IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds

The parasite species of genus Plasmodium causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds with novel targets in the proteome of Plasmodium sp. is a very important goal for the pharmaceutical industry. We can expect that the success of the pre-clinical assay depends on the conditions of assay per se, the chemical structure of the drug, the structure of the target protein to be targeted, as well as on factors governing the expression of this protein in the proteome such as genes (Deoxyribonucleic acid, DNA) sequence and/or chromosomes structure. However, there are no reports of computational models that consider all these factors simultaneously. Some of the difficulties for this kind of analysis are the dispersion of data in different datasets, the high heterogeneity of data, etc. In this work, we analyzed three databases ChEMBL (Chemical database of the European Molecular Biology Laboratory), UniProt (Universal Protein Resource), and NCBI-GDV (National Center for Biotechnology Information—Genome Data Viewer) to achieve this goal. The ChEMBL dataset contains outcomes for 17,758 unique assays of potential Antimalarial compounds including numeric descriptors (variables) for the structure of compounds as well as a huge amount of information about the conditions of assays. The NCBI-GDV and UniProt datasets include the sequence of genes, proteins, and their functions. In addition, we also created two partitions (c_assayj = c_aj and c_dataj = cd_j) of categorical variables from theChEMBL dataset. These partitions contain variables that encode information about experimental conditions of preclinical assays (c_aj) or about the nature and quality of data (c_dj). These categorical variables include information about 22 parameters of biological activity (c_a0), 28 target proteins (c_a1), and 9 organisms of assay (c_a2), etc. We also created another partition of (c_protj = c_pj) including categorical variables with biological information about the target proteins, genes, and chromosomes. These variables cover32 genes (c_p0), 10 chromosomes (c_p1), gene orientation (c_p2), and 31 protein functions (c_p3). We used a Perturbation-Theory Machine Learning Information Fusion (IFPTML) algorithm to map all this information (from three databases) into and train a predictive model. Shannon’s entropy measure Sh_k (numerical variables) was used to quantify the information about the structure of drugs, protein sequences, gene sequences, and chromosomes in the same information scale. Perturbation Theory Operators (PTOs) with the form of Moving Average (MA) operators have been used to quantify perturbations (deviations) in the structural variables with respect to their expected values for different subsets (partitions) of categorical variables. We obtained three IFPTML models using General Discriminant Analysis (GDA), Classification Tree with Univariate Splits (CTUS), and Classification Tree with Linear Combinations (CTLC). The IFPTML-CTLC presented the better performance with Sensitivity Sn(%) = 83.6/85.1, and Specificity Sp(%) = 89.8/89.7 for training/validation sets, respectively. This model could become a useful tool for the optimization of preclinical assays of new Antimalarial compounds vs. different proteins in the proteome of Plasmodium.

Towards machine learning discovery of dual antibacterial drug-nanoparticle systems

Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165 000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%-99.0% and AUROC ≈ 0.94-0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL^-1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5-100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.

Computational Drug Repurposing for Antituberculosis Therapy: Discovery of Multi-Strain Inhibitors

Tuberculosis remains the most afflicting infectious disease known by humankind, with one quarter of the population estimated to have it in the latent state. Discovering antituberculosis drugs is a challenging, complex, expensive, and time-consuming task. To overcome the substantial costs and accelerate drug discovery and development, drug repurposing has emerged as an attractive alternative to find new applications for “old” drugs and where computational approaches play an essential role by filtering the chemical space. This work reports the first multi-condition model based on quantitative structure–activity relationships and an ensemble of neural networks (mtc-QSAR-EL) for the virtual screening of potential antituberculosis agents able to act as multi-strain inhibitors. The mtc-QSAR-EL model exhibited an accuracy higher than 85%. A physicochemical and fragment-based structural interpretation of this model was provided, and a large dataset of agency-regulated chemicals was virtually screened, with the mtc-QSAR-EL model identifying already proven antituberculosis drugs while proposing chemicals with great potential to be experimentally repurposed as antituberculosis (multi-strain inhibitors) agents. Some of the most promising molecules identified by the mtc-QSAR-EL model as antituberculosis agents were also confirmed by another computational approach, supporting the capabilities of the mtc-QSAR-EL model as an efficient tool for computational drug repurposing.

A Short-Course Antibiotic Prophylaxis Is Associated with Limited Antibiotic Resistance Emergence in Post-Operative Infection of Pelvic Primary Bone Tumor Resection

Resections of primary pelvic bone tumors are frequently complicated by surgical site infections (SSIs), thereby impairing the functional prognosis of patients, especially in case of implant removal. Although prophylactic antibiotics play an essential role in preventing SSIs, there are presently no recommendations that support their appropriate use. This study aimed to assess the impact of a 24 h prophylactic protocol on the bacterial ecology, the resistance pattern, and the SSI healing rate. We hypothesized that this protocol not only limits the emergence of resistance but also results in a good cure rate with implant retention in case of SSI. A retrospective study was performed that included all patients with an SSI following a pelvic bone tumoral resection between 2005 and 2017 who received a 24 h antibiotic prophylaxis protocol. Twenty-nine patients with an SSI were included. We observed a 75.9% rate of polymicrobial infection, with a high prevalence of digestive flora microorganisms and a majority of wild-type phenotypes. We confirmed that there was no significant emergence of resistant flora. After first-line debridement, antibiotics (DA) if any implant was used, or debridement, antibiotics, and implant retention (DAIR) whenever possible, we obtained a 79.3% cure rate, with implant removal in 20% of cases. The absence of an implant was significantly associated with SSI healing. Early infection management and low resistance profiles may also have a positive effect, but this needs to be confirmed in a larger cohort. In light of this, the use of a 24 h prophylactic protocol in primary pelvic bone tumor resections is associated with a favorable infection cure rate and implant retention in case of SSI, and minimal selection of resistant microorganisms.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Palladium-mediated synthesis and biological evaluation of C-10b substituted Dihydropyrrolo[1,2-b]isoquinolines as antileishmanial agents

The development of new molecules for the treatment of leishmaniasis is, a neglected parasitic disease, is urgent as current anti-leishmanial therapeutics are hampered by drug toxicity and resistance. The pyrrolo[1,2-b]isoquinoline core was selected as starting point, and palladium-catalyzed Heck-initiated cascade reactions were developed for the synthesis of a series of C-10 substituted derivatives. Their in vitro leishmanicidal activity against visceral (L. donovani) and cutaneous (L. amazonensis) leishmaniasis was evaluated. The best activity was found, in general, for the 10-arylmethyl substituted pyrroloisoquinolines. In particular, 2ad (IC₅₀ = 3.30 μM, SI > 77.01) and 2bb (IC₅₀ = 3.93 μM, SI > 58.77) were approximately 10-fold more potent and selective than the drug of reference (miltefosine), against L. amazonensis on in vitro promastigote assays, while 2ae was the more active compound in the in vitro amastigote assays (IC₅₀ = 33.59 μM, SI > 8.93). Notably, almost all compounds showed low cytotoxicity, CC₅₀ > 100 μg/mL in J774 cells, highest tested dose. In addition, we have developed the first Perturbation Theory Machine Learning (PTML) algorithm able to predict simultaneously multiple biological activity parameters (IC₅₀, K_i, etc.) vs. any Leishmania species and target protein, with high values of specificity (>98%) and sensitivity (>90%) in both training and validation series. Therefore, this model may be useful to reduce time and assay costs (material and human resources) in the drug discovery process.

Molecular docking, SAR analysis and biophysical approaches in the study of the antibacterial activity of ceramides isolated from Cissus incisa

The developing of antibacterial resistance is becoming in crisis. In this sense, natural products play a fundamental role in the discovery of antibacterial agents with diverse mechanisms of action. Phytochemical investigation of Cissus incisa leaves led to isolation and characterization of the ceramides mixture (1): (8E)-2-(tritriacont-9-enoyl amino)-1,3,4-octadecanetriol-8-ene (1-I); (8E)-2-(2',3'-dihydroxyoctacosanoyl amino)-1,3,4-octadecanetriol-8-ene (1-II); (8E)-2-(2'-hydroxyheptacosanoyl amino)-1,3,4-octadecanetriol-8-ene (1-III); and (8E)-2-(-2'-hydroxynonacosanoyl amino)-1,3,4-octadecanetriol-8-ene (1-IV). Until now, this is the first report of the ceramides (1-I), (1-II), and (1-IV). The structures were elucidated using NMR and mass spectrometry analyses. Antibacterial activity of ceramides (1) and acetylated derivates (2) was evaluated against nine multidrug-resistant bacteria by Microdilution method. (1) showed the best results against Gram-negatives, mainly against carbapenems-resistant Acinetobacter baumannii with MIC = 50 μg/mL. Structure-activity analysis and molecular docking revealed interactions between plant ceramides with membrane proteins, and enzymes associated with biological membranes of Gram-negative bacteria, through hydrogen bonding of functional groups. Vesicular contents release assay showed the capacity of (1) to disturb membrane permeability detected by an increase of fluorescence probe over time. The membrane disruption is not caused for ceramides lytic action on cell membranes, according in vitro hemolyticactivity results. Combining SAR analysis, bioinformatics and biophysical techniques, and also experimental tests, it was possible to explain the antibacterial action of these natural ceramides.

IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks

Nanoparticles are useful antimicrobial drug-release systems, but some nanoparticles also exhibit antibacterial activity. However, investigation of their antibacterial activity is a difficult and slow process due to the numerous combinations of nanoparticle size, shape, and composition vs. biological tests, assay organisms, and multiple activity parameters to be measured. Additionally, the overuse of antibiotics has led to the emergence of resistant bacterial strains with different metabolic networks. Computational models may speed up this process, but the models reported to date do not to consider all the previous factors, and the data sources are dispersed and not curated. Thus, herein, we used an information fusion, perturbation-theory machine learning (IFPTML) approach, which is introduced by us for the first time, to fit a model for the discovery of antibacterial nanoparticles. The dataset studied had 15 classes of nanoparticles (1-100 nm) with most cases in the range of 1-50 nm vs. >20 pathogenic bacteria species with different metabolic networks. The nanoparticles studied included metal nanoparticles of Au, Ag, and Cu; oxide nanoparticles of Zn, Cu, La, Al, Fe, Sn, Ti, Cd, and Si; and metal salt nanoparticles of CuI and CdS. We used the SOFT.PTML software (our own application) with a user-friendly interface for the IFPTML calculations and a control statistics package. Using SOFT.PTML, we found a linear logistic regression equation that could model 4 biological activity parameters using only 8 variables with χ2 = 2265.75, p-level <0.05, sensitivity, Sn = 79.4, and specificity, Sp = 99.3, for 3213 cases (nanoparticle-bacteria pairs) in the training series. The model had Sn = 80.8 and Sp = 99.3 for 2114 cases in the external validation series. We also developed a random forest non-linear model with higher values of Sn and Sp = 98-99% in the training/validation series, although it was more complicated to use. SOFT.PTML has been demonstrated to be a useful tool for the analysis of complex data in nanotechnology. We also introduced a new anabolism-catabolism unbalance index of metabolic networks to reveal the biological connotation of the IFPTML predictions for antibacterial nanoparticles. These new models open a new door for the discovery of NPs vs. new bacterial species and strains with different topological structures of their metabolic networks.

Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines

Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.

PTML Modeling for Alzheimer’s Disease: Design and Prediction of Virtual Multi-Target Inhibitors of GSK3B, HDAC1, and HDAC6

Background:

Alzheimer’s disease is characterized by a progressive pattern of cognitive and functional impairment, which ultimately leads to death. Computational approaches have played an important role in the context of drug discovery for anti-Alzheimer's therapies. However, most of the computational models reported to date have been focused on only one protein associated with Alzheimer's, while relying on small datasets of structurally related molecules.

Objective:

We introduce the first model combining perturbation theory and machine learning based on artificial neural networks (PTML-ANN) for simultaneous prediction and design of inhibitors of three Alzheimer’s disease-related proteins, namely glycogen synthase kinase 3 beta (GSK3B), histone deacetylase 1 (HDAC1), and histone deacetylase 6 (HDAC6).

Methods:

The PTML-ANN model was obtained from a dataset retrieved from ChEMBL, and it relied on a classification approach to predict chemicals as active or inactive.

Results:

The PTML-ANN model displayed sensitivity and specificity higher than 85% in both training and test sets. The physicochemical and structural interpretation of the molecular descriptors in the model permitted the direct extraction of fragments suggested to favorably contribute to enhancing the multitarget inhibitory activity. Based on this information, we assembled ten molecules from several fragments with positive contributions. Seven of these molecules were predicted as triple target inhibitors while the remaining three were predicted as dual-target inhibitors. The estimated physicochemical properties of the designed molecules complied with Lipinski’s rule of five and its variants.

Conclusion:

This work opens new horizons toward the design of multi-target inhibitors for anti- Alzheimer's therapies.

Synthesis, pharmacological profile and 2D-QSAR studies of curcumin-amino acid conjugates as potential drug candidates

A series of curcumin bis-conjugates 3a-q, 5a-k and 6a-k were synthesized in good yields utilizing an optimized reaction condition. We explored the effect of different amino acids and protecting groups on biological activities of curcumin. The conjugates were screened for anti-inflammatory, analgesic and antimicrobial properties. Some of the conjugates showed promising biological observations with a potency comparable with the standard references. The variations in biological properties concerning different amino acids and protecting groups are interesting observations. Effects of the synthesized conjugates on splenocytes and the production of nitric oxide by lipopolysaccharide-stimulated peritoneal macrophages are correlated with the observed anti-inflammatory properties. We have also established the safety profile of the most active conjugates. Robust 2D-QSAR studies supported and validated biological data.

Antibacterial Activity of Cissus incisa Extracts against Multidrug- Resistant Bacteria

Aims:

The need to find new antimicrobial agents to cope with this phenomenon increases.

Background:

Infection diseases are illness caused by different microorganisms, such as bacteria, among those caused by resistant bacteria are associated with greater morbidity, mortality and cost of the treatment than those caused by sensitive bacteria of the same species.

Objective:

Need to find new antimicrobial agents to cope with this phenomenon increases.

Method:

This work carried out the study of biological activities of Cissus incisa, taking account its traditional use. Three extracts were prepared from the leaves of this plant: hexane, chloroform methanol (1:1) and aqueous. Their antibacterial and antitubercular activities were evaluated using microdilution and alamar blue assays; respectively.

Result:

The chloroform/methanol extract (1:1) was the most active of the three tested extracts for antimicrobial activity. In this way, the extract exhibits a broad spectrum of antimicrobial activity, against the Gram positive and Gram negative bacteria tested, with MIC values between 125 to 500 μg/mL.

Conclusion:

This research contributes both to the knowledge of the Mexican flora, as well as the discovery of potential antibacterial agents derivate from plants.

Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays

G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory (ChEMBL) database. These data are also very complex covering changes in drug chemical structure and assay conditions like c₀ = activity parameter (K_i, IC₅₀, etc.), c₁ = target protein, c₂ = cell line, c₃ = assay organism, etc., making difficult the analysis of these databases that are placed in the borders of a Big Data challenge. One of the aims of this work is to develop a computational model able to predict new GPCRs targeting drugs taking into consideration multiple conditions of assay. Another objective is to perform new predictive and experimental studies of selective 5-HTA2 receptor agonist, antagonist, or inverse agonist in human comparing the results with those from the literature. In this work, we combined Perturbation Theory (PT) and Machine Learning (ML) to seek a general PTML model for this data set. We analyzed 343 738 unique compounds with 812 072 end points (assay outcomes), with 185 different experimental parameters, 592 protein targets, 51 cell lines, and/or 55 organisms (species). The best PTML linear model found has three input variables only and predicted 56 202/58 653 positive outcomes (sensitivity = 95.8%) and 470 230/550 401 control cases (specificity = 85.4%) in training series. The model also predicted correctly 18 732/19 549 (95.8%) of positive outcomes and 156 739/183 469 (85.4%) of cases in external validation series. To illustrate its practical use, we used the model to predict the outcomes of six different 5-HT2A receptor drugs, namely, TCB-2, DOI, DOB, altanserin, pimavanserin, and nelotanserin, in a very large number of different pharmacological assays. 5-HT2A receptors are altered in schizophrenia and represent drug target for antipsychotic therapeutic activity. The model correctly predicted 93.83% (76 of 86) experimental results for these compounds reported in ChEMBL. Moreover, [³⁵S]GTPγS binding assays were performed experimentally with the same six drugs with the aim of determining their potency and efficacy in the modulation of G-proteins in human brain tissue. The antagonist ketanserin was included as inactive drug with demonstrated affinity for 5-HT2A/C receptors. Our results demonstrate that some of these drugs, previously described as serotonin 5-HT2A receptor agonists, antagonists, or inverse agonists, are not so specific and show different intrinsic activity to that previously reported. Overall, this work opens a new gate for the prediction of GPCRs targeting compounds.

Multilabel and Missing Label Methods for Binary Quantitative Structure-Activity Relationship Models: An Application for the Prediction of Adverse Drug Reactions

The prediction of adverse drug reactions in the discovery of new medicines is highly challenging. In the task of predicting the adverse reactions of chemical compounds, information about different targets is often available. Although we can focus on every adverse drug reaction prediction separately, multilabel approaches have been proven useful in many research areas for taking advantage of the relationship among the targets. However, when approaching the prediction problem from a multilabel point of view, we have to deal with the lack of information for some labels. This missing labels problem is a relevant issue in the field of cheminformatics approaches. This paper aims to predict the adverse drug reaction of commercial drugs using a multilabel approach where the possible presence of missing labels is also taken into consideration. We propose the use of multilabel methods to deal with the prediction of a large set of 27 different adverse reaction targets. We also propose the use of multilabel methods specifically designed to deal with the missing labels problem to test their ability to solve this difficult problem. The results show the validity of the proposed approach, demonstrating a superior performance of the multilabel method compared with the single-label approach in addressing the problem of adverse drug reaction prediction.

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for retroviral infections. For this reason, we propose to develop a new predictive model combining perturbation theory (PT) bases and machine learning (ML) modeling to create a new tool that can take advantage of all the available information. The PTML model proposed in this work for the ChEMBL data set preclinical experimental assays for antiretroviral compounds consists of a linear equation with four variables. The PT operators used are founded on multicondition moving averages, combining different features and simplifying the difficulty to manage all data. More than 140 000 preclinical assays for 56 105 compounds with different characteristics or experimental conditions have been carried out and can be found in ChEMBL database, covering combinations with 359 biological activity parameters (c₀), 55 protein accessions (c₁), 83 cell lines (c₂), 64 organisms of assay (c₃), and 773 subtypes or strains. We have included 150 148 preclinical experimental assays for HIV virus, 1188 for HTLV virus, 84 for simian immunodeficiency virus, 370 for murine leukemia virus, 119 for Rous sarcoma virus, 1581 for MMTV, etc. We also included 5277 assays for hepatitis B virus. The developed PTML model reached considerable values in sensibility (73.05% for training and 73.10% for validation), specificity (86.61% for training and 87.17% for validation), and accuracy (75.84% for training and 75.98% for validation). We also compared alternative PTML models with different PT operators such as covariance, moments, and exponential terms. Finally, we made a comparison between literature ML models with our PTML model and also artificial neural network (ANN) nonlinear models. We conclude that this PTML model is the first one to consider multiple characteristics of preclinical experimental antiretroviral assays combined, generating a simple, useful, and adaptable instrument, which could reduce time and costs in antiretroviral drugs research.

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.