In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach

Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms

Alga toxins have recently emerged as environmental risk factors to multiple human health issues. Mitochondrial toxicity is an essential element in the field of ecotoxicology, it is necessary to screen and manage mitochondrial toxicants from common alga toxins. To overcome the limitations of traditional animal and cell experiments, computational toxicology is increasingly emphasized. In this study, all the publicly available datasets were compiled to create the largest mitochondrial toxicity dataset to date, establishing a robust and high-performance QSAR screening model. The model couples and filters 12 molecular fingerprints and 318 descriptors as features, capturing more information about molecular structure and properties. By comparing 8 machine learning algorithms and using a weighted soft voting method to integrate the two optimal algorithms, we established 108 prediction models and identified the best ensemble learning model MACCS_LK for screening and defining its application domain. Additionally, the efficacy of MACCS fingerprints in representing mitochondrial toxicants was established, and a mechanistic analysis of the identified model based on the SHAP method and 11 structural alerts uncovered in this study was conducted, enhancing the interpretability of this model. This study highlights the key roles of lipophilic structures such as aromatic rings and long hydrocarbon chains and their related physicochemical properties in predicting toxicity outcomes. The mitochondrial toxicity of six algal toxins was predicted by employing this model, and the results indicating that two of them possess mitochondrial toxic effects. This model has high reliability and accuracy, making it applicable for predicting mitochondrial toxicity of more marine biotoxins.

Developing a GNN-based AI model to predict mitochondrial toxicity using the bagging method

Mitochondrial toxicity has been implicated in the development of various toxicities, including hepatotoxicity. Therefore, mitochondrial toxicity has become a major screening factor in the early discovery phase of drug development. Several models have been developed to predict mitochondrial toxicity based on chemical structures. However, they only provide a binary classification of positive or negative results and do not provide the substructures that contribute to a positive decision. Therefore, we developed an artificial intelligence (AI) model to predict mitochondrial toxicity and visualize structural alerts. To construct the model, we used the open-source software library kMoL, which employs a graph neural network approach that allows learning from chemical structure data. We also utilized the integrated gradient method, which enables the visualization of substructures that contribute to positive results. The dataset used to construct the AI model exhibited a significant imbalance, with significantly more negative than positive data. To address this, we employed the bagging method, which resulted in a model with high predictive performance, as evidenced by an F1 score of 0.839. This model can also be used to visualize substructures that contribute to mitochondrial toxicity using the integrated gradient method. Our AI model predicts mitochondrial toxicity based on chemical structures and may contribute to screening mitochondrial toxicity in the early stages of drug discovery.

Synthesis and in silico study of chenodeoxycholic acid and its analogues as an alternative inhibitor of spike glycoprotein of SARS-CoV-2

COVID-19, caused by SARS-CoV-2, is a viral infection that has generated one of the most significant health problems in the world. Spike glycoprotein is a crucial enzyme in viral replication and transcription mediation. There are reports in the literature on using bile acid in the fight against this virus through in vitro tests. This work presents the synthesis of nine chenodeoxycholic acid derivatives (1-9), which were prepared by oxidation, acetylation, formylation, and esterification reactions, and the analogs 6-9 have not yet been reported in the literature and the possibility of conducting an in silico study of bile acid derivatives as a therapeutic alternative to combat the virus using glycoprotein as a macromolecular target. As a result, five compounds (1, 6-9) possessed favorable competitive interactions with the lowest energies compared to the native ligand (BLA), and the highlighted compound 9 got the best scores. At the same time, analog 1 presented the best ADME filter result. Molecular dynamics also simulated these compounds to verify their stability within the active protein site to seek new therapeutic propositions to fight against the pandemic. Physical and spectroscopic data have fully characterized all the compounds.Communicated by Ramaswamy H. Sarma.

Modeling and Insights into the Structural Characteristics of Chemical Mitochondrial Toxicity

Mitochondria are the energy metabolism center of cells and are involved in a number of other processes, such as cell differentiation and apoptosis, signal transduction, and regulation of cell cycle and cell proliferation. It is of great significance to evaluate the mitochondrial toxicity of drugs and other chemicals. In the present study, we aimed to propose easily available artificial intelligence (AI) models for the prediction of chemical mitochondrial toxicity and investigate the structural characteristics with the analysis of molecular properties and structural alerts. The consensus model achieved good predictive results with high total accuracy at 87.21% for validation sets. The models can be accessed freely via https://ochem.eu/article/158582. Besides, several commonly used chemical properties were significantly different between chemicals with and without mitochondrial toxicity. We also detected the structural alerts (SAs) responsible for mitochondrial toxicity and integrated them into the web-server SApredictor (www.sapredictor.cn). The study may provide useful tools for in silico estimation of mitochondrial toxicity and be helpful to understand the mechanisms of mitochondrial toxicity.

Prediction of drug-induced mitochondrial dysfunction using succinate-cytochrome c reductase activity, QSAR and molecular docking

There is increasing evidence that links mitochondrial off-target effects with organ toxicities. For this reason, predictive strategies need to be developed to identify mitochondrial dysfunction early in the drug discovery process. In this study, as a major mechanism of mitochondrial toxicity, first, the inhibitory activity of 35 compounds against succinate-cytochrome c reductase (SCR) was investigated. This in vitro study led to the generation of consistent experimental data for a diverse range of compounds, including pharmaceutical drugs and fungicides. Next, molecular docking and protein-ligand interaction fingerprinting (PLIF) analysis were used to identify significant residues and protein-ligand interactions for the Q_o site of complex III and Q site of complex II. Finally, this data was used for the development of QSAR models using a regression-based approach to highlight structural and chemical features that might be responsible for SCR inhibition. The statistically validated QSAR models from this work highlighted the importance of low aqueous solubility, low ionisation, fewer 6-membered rings and shorter hydrocarbon alkane chains in the molecular structure for increased inhibition of SCR, hence mitochondrial toxicity. PLIF analysis highlighted two key residues for inhibitory activity of the Q_o site of complex III: His 161 as H-bond acceptor and Pro 270 for arene interactions. Currently, there are limited structure-activity models published in the scientific literature for the prediction of mitochondrial toxicity. We believe this study helps shed light on the chemical space for the inhibition of mitochondrial electron transport chain (ETC).

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.

XML-CIMT: Explainable Machine Learning (XML) Model for Predicting Chemical-Induced Mitochondrial Toxicity

Organ toxicity caused by chemicals is a serious problem in the creation and usage of chemicals such as medications, insecticides, chemical products, and cosmetics. In recent decades, the initiation and development of chemical-induced organ damage have been related to mitochondrial dysfunction, among several adverse effects. Recently, many drugs, for example, troglitazone, have been removed from the marketplace because of significant mitochondrial toxicity. As a result, it is an urgent requirement to develop in silico models that can reliably anticipate chemical-induced mitochondrial toxicity. In this paper, we have proposed an explainable machine-learning model to classify mitochondrially toxic and non-toxic compounds. After several experiments, the Mordred feature descriptor was shortlisted to be used after feature selection. The selected features used with the CatBoost learning algorithm achieved a prediction accuracy of 85% in 10-fold cross-validation and 87.1% in independent testing. The proposed model has illustrated improved prediction accuracy when compared with the existing state-of-the-art method available in the literature. The proposed tree-based ensemble model, along with the global model explanation, will aid pharmaceutical chemists in better understanding the prediction of mitochondrial toxicity.

Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection

Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 244 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity. Our results suggest that combining chemical descriptors with biological readouts enhances the detection of mitochondrial toxicants, with practical implications in drug discovery.

Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature

PROteolysis TArgeting Chimeras (PROTACs) use the ubiquitin-proteasome system to degrade a protein of interest for therapeutic benefit. Advances made in targeted protein degradation technology have been remarkable, with several molecules having moved into clinical studies. However, robust routes to assess and better understand the safety risks of PROTACs need to be identified, which is an essential step toward delivering efficacious and safe compounds to patients. In this work, we used Cell Painting, an unbiased high-content imaging method, to identify phenotypic signatures of PROTACs. Chemical clustering and model prediction allowed the identification of a mitotoxicity signature that could not be expected by screening the individual PROTAC components. The data highlighted the benefit of unbiased phenotypic methods for identifying toxic signatures and the potential to impact drug design.

Prescription Drugs and Mitochondrial Metabolism

Mitochondria are central to the physiology and survival of nearly all eukaryotic cells and house diverse metabolic processes including oxidative phosphorylation, reactive oxygen species buffering, metabolite synthesis/exchange, and Ca2+ sequestration. Mitochondria are phenotypically heterogeneous and this variation is essential to the complexity of physiological function among cells, tissues, and organ systems. As a consequence of mitochondrial integration with so many physiological processes, small molecules that modulate mitochondrial metabolism induce complex systemic effects. In the case of many common prescribed drugs, these interactions may contribute to drug therapeutic mechanisms, induce adverse drug reactions, or both. The purpose of this article is to review historical and recent advances in the understanding of the effects of prescription drugs on mitochondrial metabolism. Specific 'modes' of xenobiotic-mitochondria interactions are discussed to provide a set of qualitative models that aid in conceptualizing how the mitochondrial energy transduction system may be affected. Findings of recent in vitro high-throughput screening studies are reviewed, and a few candidate drug classes are chosen for additional brief discussion (i.e. antihyperglycemics, antidepressants, antibiotics, and antihyperlipidemics). Finally, recent improvements in pharmacokinetic models that aid in quantifying systemic effects of drug-mitochondria interactions are briefly considered.

Screening of 6000 Compounds for Uncoupling Activity: A Comparison Between a Mechanistic Biophysical Model and the Structural Alert Profiler Mitotox

Protonophoric uncoupling of phosphorylation is an important factor when assessing chemicals for their toxicity, and has recently moved into focus in pharmaceutical research with respect to the treatment of diseases such as cancer, diabetes, or obesity. Reliably identifying uncoupling activity is thus a valuable goal. To that end, we screened more than 6000 anionic compounds for in vitro uncoupling activity, using a biophysical model based on ab initio COSMO-RS input parameters with the molecular structure as the only external input. We combined these results with a model for baseline toxicity (narcosis). Our model identified more than 1250 possible uncouplers in the screening dataset, and identified possible new uncoupler classes such as thiophosphoric acids. When tested against 423 known uncouplers and 612 known inactive compounds in the dataset, the model reached a sensitivity of 83% and a specificity of 96%. In a direct comparison, it showed a similar specificity than the structural alert profiler Mitotox (97%), but much higher sensitivity than Mitotox (47%). The biophysical model thus allows for a more accurate screening for uncoupling activity than existing structural alert profilers. We propose to use our model as a complementary tool to screen large datasets for protonophoric uncoupling activity in drug development and toxicity assessment.

Mapping the cellular response to electron transport chain inhibitors reveals selective signaling networks triggered by mitochondrial perturbation

Mitochondrial perturbation is a key event in chemical-induced organ toxicities that is incompletely understood. Here, we studied how electron transport chain (ETC) complex I, II, or III (CI, CII and CIII) inhibitors affect mitochondrial functionality, stress response activation, and cell viability using a combination of high-content imaging and TempO-Seq in HepG2 hepatocyte cells. CI and CIII inhibitors perturbed mitochondrial membrane potential (MMP) and mitochondrial and cellular ATP levels in a concentration- and time-dependent fashion and, under conditions preventing a switch to glycolysis attenuated cell viability, whereas CII inhibitors had no effect. TempO-Seq analysis of changes in mRNA expression pointed to a shared cellular response to CI and CIII inhibition. First, to define specific ETC inhibition responses, a gene set responsive toward ETC inhibition (and not to genotoxic, oxidative, or endoplasmic reticulum stress) was identified using targeted TempO-Seq in HepG2. Silencing of one of these genes, NOS3, exacerbated the impact of CI and CIII inhibitors on cell viability, indicating its functional implication in cellular responses to mitochondrial stress. Then by monitoring dynamic responses to ETC inhibition using a HepG2 GFP reporter panel for different classes of stress response pathways and applying pathway and gene network analysis to TempO-Seq data, we looked for downstream cellular events of ETC inhibition and identified the amino acid response (AAR) as being triggered in HepG2 by ETC inhibition. Through in silico approaches we provide evidence indicating that a similar AAR is associated with exposure to mitochondrial toxicants in primary human hepatocytes. Altogether, we (i) unravel quantitative, time- and concentration-resolved cellular responses to mitochondrial perturbation, (ii) identify a gene set associated with adaptation to exposure to active ETC inhibitors, and (iii) show that ER stress and an AAR accompany ETC inhibition in HepG2 and primary hepatocytes.

In silico prediction of mitochondrial toxicity of chemicals using machine learning methods

Mitochondria are important organelles in human cells, providing more than 95% of the energy. However, some drugs and environmental chemicals could induce mitochondrial dysfunction, which might cause complex diseases and even worsen the condition of patients with mitochondrial damage. Some drugs have been withdrawn from the market due to their severe mitochondrial toxicity, such as troglitazone. Therefore, there is an urgent need to develop models that could accurately predict the mitochondrial toxicity of chemicals. In this paper, suitable data were obtained from literature and databases first. Then nine types of fingerprints were used to characterize these compounds. Finally, different algorithms were used to build models. Meanwhile, the applicability domain of the prediction models was defined. We have also explored the structural alerts of mitochondrial toxicity, which would be helpful for medicinal chemists to better predict mitochondrial toxicity and further optimize lead compounds.

Discriminant models on mitochondrial toxicity improved by consensus modeling and resolving imbalance in training

Humans and animals may be exposed to tens of thousands of natural and synthetic chemicals during their lifespan. It is difficult to assess risk for all the chemicals with experimental toxicity tests. An alternative approach is to use computational toxicology methods such as quantitative structure-activity relationship (QSAR) modeling. Mitochondrial toxicity is involved in many diseases such as cancer, neurodegeneration, type 2 diabetes, cardiovascular diseases and autoimmune diseases. Thus, it is important to rapidly and efficiently identify chemicals with mitochondrial toxicity. In this study, five machine learning algorithms and twelve types of molecular fingerprints were employed to generate QSAR discriminant models for mitochondrial toxicity. A threshold moving method was adopted to resolve the imbalance issue in the training data. Consensus of the models by an averaging probability strategy improved prediction performance. The best model has correct classification rates of 81.8% and 88.3% in ten-fold cross validation and external validation, respectively. Substructures such as phenol, carboxylic acid, nitro and arylchloride were found informative through analysis of information gain and frequency of substructures. The results demonstrate that resolving imbalance in training and building consensus models can improve classification rates for mitochondrial toxicity prediction.

Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity

Over the last few years more and more organ and idiosyncratic toxicities were linked to mitochondrial toxicity. Despite well-established assays, such as the seahorse and Glucose/Galactose assay, an in silico approach to mitochondrial toxicity is still feasible, particularly when it comes to the assessment of large compound libraries. Therefore, in silico approaches could be very beneficial to indicate hazards early in the drug development pipeline. By combining multiple endpoints, we derived the largest so far published dataset on mitochondrial toxicity. A thorough data analysis shows that molecules causing mitochondrial toxicity can be distinguished by physicochemical properties. Finally, the combination of machine learning and structural alerts highlights the suitability for in silico risk assessment of mitochondrial toxicity.

An Analysis of QSAR Research Based on Machine Learning Concepts

Quantitative Structure-Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called 'ML-QSAR'. This framework has been designed for future research to: a) facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.

Toxicity Prediction Method Based on Multi-Channel Convolutional Neural Network

Molecular toxicity prediction is one of the key studies in drug design. In this paper, a deep learning network based on a two-dimension grid of molecules is proposed to predict toxicity. At first, the van der Waals force and hydrogen bond were calculated according to different descriptors of molecules, and multi-channel grids were generated, which could discover more detail and helpful molecular information for toxicity prediction. The generated grids were fed into a convolutional neural network to obtain the result. A Tox21 dataset was used for the evaluation. This dataset contains more than 12,000 molecules. It can be seen from the experiment that the proposed method performs better compared to other traditional deep learning and machine learning methods.

Opportunities and challenges using artificial intelligence in ADME/Tox

A recent conference organized a panel of scientists representing small and big pharma companies, who work at the interface of machine learning (ML) and absorption, distribution, metabolism, excretion, and toxicology (ADME/Tox). With the recent rebirth of AI related to pharma, it is timely to present this collaborative commentary to capture the diverging opinions on the past, present and future role of AI for ADME/Tox and how it can be applied in newer areas such as nanomaterials.

Evaluating parameters for ligand-based modeling with random forest on sparse data sets

Ligand-based predictive modeling is widely used to generate predictive models aiding decision making in e.g. drug discovery projects. With growing data sets and requirements on low modeling time comes the necessity to analyze data sets efficiently to support rapid and robust modeling. In this study we analyzed four data sets and studied the efficiency of machine learning methods on sparse data structures, utilizing Morgan fingerprints of different radii and hash sizes, and compared with molecular signatures descriptor of different height. We specifically evaluated the effect these parameters had on modeling time, predictive performance, and memory requirements using two implementations of random forest; Scikit-learn as well as FEST. We also compared with a support vector machine implementation. Our results showed that unhashed fingerprints yield significantly better accuracy than hashed fingerprints ([Formula: see text]), with no pronounced deterioration in modeling time and memory usage. Furthermore, the fast execution and low memory usage of the FEST algorithm suggest that it is a good alternative for large, high dimensional sparse data. Both support vector machines and random forest performed equally well but results indicate that the support vector machine was better at using the extra information from larger values of the Morgan fingerprint's radius.

Naïve Bayesian Models for Vero Cell Cytotoxicity

PURPOSE

To advance translational research of potential therapeutic small molecules against infectious microbes, the compounds must display a relative lack of mammalian cell cytotoxicity. Vero cell cytotoxicity (CC₅₀) is a common initial assay for this metric. We explored the development of naïve Bayesian models that can enhance the probability of identifying non-cytotoxic compounds.

METHODS

Vero cell cytotoxicity assays were identified in PubChem, reformatted, and curated to create a training set with 8741 unique small molecules. These data were used to develop Bayesian classifiers, which were assessed with internal cross-validation, external tests with a set of 193 compounds from our laboratory, and independent validation with an additional diverse set of 1609 unique compounds from PubChem.

RESULTS

Evaluation with independent, external test and validation sets indicated that cytotoxicity Bayesian models constructed with the ECFP_6 descriptor were more accurate than those that used FCFP_6 fingerprints. The best cytotoxicity Bayesian model displayed predictive power in external evaluations, according to conventional and chance-corrected statistics, as well as enrichment factors.

CONCLUSIONS

The results from external tests demonstrate that our novel cytotoxicity Bayesian model displays sufficient predictive power to help guide translational research. To assist the chemical tool and drug discovery communities, our curated training set is being distributed as part of the Supplementary Material. Graphical Abstract Naive Bayesian models have been trained with publically available data and offer a useful tool for chemical biology and drug discovery to select for small molecules with a high probability of exhibiting acceptably low Vero cell cytotoxicity.

Development of novel prediction model for drug-induced mitochondrial toxicity by using naïve Bayes classifier method

Mitochondrial dysfunction has been considered as an important contributing factor in the etiology of drug-induced organ toxicity, and even plays an important role in the pathogenesis of some diseases. The objective of this investigation was to develop a novel prediction model of drug-induced mitochondrial toxicity by using a naïve Bayes classifier. For comparison, the recursive partitioning classifier prediction model was also constructed. Among these methods, the prediction performance of naïve Bayes classifier established here showed best, which yielded average overall prediction accuracies for the internal 5-fold cross validation of the training set and external test set were 95 ± 0.6% and 81 ± 1.1%, respectively. In addition, four important molecular descriptors and some representative substructures of toxicants produced by ECFP_6 fingerprints were identified. We hope the established naïve Bayes prediction model can be employed for the mitochondrial toxicity assessment, and these obtained important information of mitochondrial toxicants can provide guidance for medicinal chemists working in drug discovery and lead optimization.

Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine

BACKGROUND

Elimination half-life is an important pharmacokinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The experimental evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life.

METHODS

In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold.

RESULTS

Among seven individual models, GBM showed the best performance (R(2)=0.820 and RMSE=0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R(2)=0.738 and RMSE=0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed.

CONCLUSIONS

An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R(2)=0.723 and RMSE=0.698).

GENERAL SIGNIFICANCE

Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Systems Pharmacology in Small Molecular Drug Discovery

Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity), target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level.

Development of an in Silico Profiler for Mitochondrial Toxicity

This study outlines the analysis of mitochondrial toxicity for a variety of pharmaceutical drugs extracted from Zhang et al. ((2009) Toxicol. In Vitro, 23, 134-140). These chemicals were grouped into categories based upon structural similarity. Subsequently, mechanistic analysis was undertaken for each category to identify the molecular initiating event driving mitochondrial toxicity. The mechanistic information elucidated during the analysis enabled mechanism-based structural alerts to be developed and combined together to form an in silico profiler. This profiler is envisaged to be used to develop chemical categories based upon similar mechanisms as part of the adverse outcome pathway paradigm. Additionally, the profiler could be utilized in screening large data sets in order to identify chemicals with the potential to induce mitochondrial toxicity.

Applying machine learning techniques for ADME-Tox prediction: a review

INTRODUCTION

Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety were considered some of the major causes of clinical failures in the development of new chemical entities. In this context, machine learning (ML) techniques have been often used in ADME-Tox studies due to the existence of compounds with known pharmacokinetic properties available for generating predictive models.

AREAS COVERED

This review examines the growth in the use of some ML techniques in ADME-Tox studies, in particular supervised and unsupervised techniques. Also, some critical points (e.g., size of the data set and type of output variable) must be considered during the generation of models that relate ADME-Tox properties and biological activity.

EXPERT OPINION

ML techniques have been successfully employed in pharmacokinetic studies, helping the complex process of designing new drug candidates from the use of reliable ML models. An application of this procedure would be the prediction of ADME-Tox properties from studies of quantitative structure-activity relationships or the discovery of new compounds from a virtual screening using filters based on results obtained from ML techniques.

Progress in computational toxicology

INTRODUCTION

Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed.

METHODS

A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods.

RESULTS

The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data.

DISCUSSION

Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications.

A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA-CG-SVM method

Breast cancer resistance protein (BCRP) is one of the key multi-drug resistance proteins, which significantly influences the therapeutic effects of many drugs, particularly anti-cancer drugs. Thus, distinguishing between substrates and non-substrates of BCRP is important not only for clinical use but also for drug discovery and development. In this study, a prediction model of the substrates and non-substrates of BCRP was developed using a modified support vector machine (SVM) method, namely GA-CG-SVM. The overall prediction accuracy of the established GA-CG-SVM model is 91.3% for the training set and 85.0% for an independent validation set. For comparison, two other machine learning methods, namely, C4.5 DT and k-NN, were also adopted to build prediction models. The results show that the GA-CG-SVM model is significantly superior to C4.5 DT and k-NN models in terms of the prediction accuracy. To sum up, the prediction model of BCRP substrates and non-substrates generated by the GA-CG-SVM method is sufficiently good and could be used as a screening tool for identifying the substrates and non-substrates of BCRP.

A human approach to drug development: opportunities and limitations

The pharmaceutical industry is failing in its primary function, with increasing expenditure and decreased output in terms of new medicines brought to market. It cannot carry on as it is, without sliding into a terminal decline. It must, therefore, take some positive steps toward addressing its problems. We do not have to look far to see one very obvious problem, namely, the industry's continuing reliance on nonhuman biology as the basis of its evaluation of potential safety and efficacy. The time has come to focus on the relevant, and to realise that more human-based testing is essential, if the industry is to survive as a source of innovation in drug therapy. This can incorporate earlier clinical testing, in the form of microdosing, and promotion of the development of more-powerful computational approaches based on human information. Fortunately, headway is being made in both approaches. However, a problem remains in the lack of functional evaluation of human tissues, where the lack of commitment, and the inadequacy of the tissue resource itself, are hampering any serious developments. An outline of a collaborative scheme is proposed, that will address this issue, central to which is improved access to research tissues from heart-beating organ donors.

Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow

The potential toxicity of chemicals may present adverse effects to the environment and human health. The quantitative structure-activity relationship (QSAR) provides a useful method for hazard assessment. In this study, we constructed a QSAR model based on a highly heterogeneous data set of 571 compounds from the US Environmental Protection Agency, for predicting acute toxicity to the fathead minnow (Pimephales promelas). An approach coupling support vector regression (SVR) with the genetic algorithm (GA) was developed to build the model. The generated QSAR model showed excellent data fitting and prediction abilities: the squared correlation coefficients (r(2)) for the training set and the test set were 0.826 and 0.802, respectively. Only eight critical descriptors, most of which are closely related to the toxicity mechanism, were chosen by GA-SVR, making the derived model readily interpretable. In summary, the successful case reported here highlights that our GA-SVR approach can be used as a general machine learning method for toxicity prediction.

Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)

Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.

In Silico Toxicology in Drug Discovery — Concepts Based on Three-dimensional Models

Animal testing is still compulsory worldwide, for the approval of drugs and chemicals produced in large quantities. Computer-assisted ( in silico) technologies are considered to be efficient alternatives to in vivo experiments, and are therefore endorsed by many regulatory agencies, e.g. for use in the European REACH initiative. Advantages of in silico methods include: the possible study of hypothetical compounds; their low cost; and the fact that such virtual experiments are typically based on human data, thus making the question of interspecies transferability obsolete. Since the mid-1990s, computer-based technologies have become an indispensable tool in drug discovery — used primarily to identify small molecules displaying a stereospecific and selective binding to a regulatory macromolecule. Since toxic effects are still responsible for some 20% of the late-stage failures, there is a continuing need for in silico concepts which can be used to estimate a compound's ADMET ( adsorption, distribution, metabolism, elimination, toxicity) properties — in particular, toxicity. The aim of this paper is to provide an insight into computational technologies that allow for the prediction of toxic effects triggered by pharmaceuticals. As most adverse and toxic effects are mediated by unwanted interactions with macromolecules involved in biological regulatory systems, we have focused on methodologies that are based on three-dimensional models of small molecules binding to such entities, and discuss the results at the molecular level.

Support vector machine and pharmacophore-based prediction models of multidrug-resistance protein 2 (MRP2) inhibitors

Overexpression of multidrug-resistance protein 2 (MRP2) is one of the main causes that lead the curative effect reduction of many drugs, particularly anticancer drugs. Development of MRP2 inhibitors is the aim to overcome multidrug resistance due to MRP2. In this study, computational prediction models of MRP2 inhibitors have been developed by using support vector machine (SVM) and pharmacophore modeling method. For the SVM model, the overall prediction accuracy is 82.9% for the training set (257 compounds) and 77.1% for the independent test set (61 compounds). And 16 descriptors have been used in the SVM modeling; but from which it is difficult to get understanding about the action mechanism. The established pharmacophore model Hypo1 consists of two hydrogen bond acceptors and one hydrophobic feature. With the use of Hypo1, 78.1% of MRP2 inhibitors and 69.6% non-inhibitors can be predicted correctly. The overall prediction accuracy is 73.9%. Although the prediction accuracy of the pharmacophore model is lower than that of SVM model, it gives a clear picture of chemical features necessary for the MRP2 inhibitors. Taken together, the SVM model is capable of predicting MRP2 inhibitors with considerable good accuracy. But the gain of action mechanism related information needs the help of pharmacophore model.