In-silico design of novel 4-aminoquinolinyl analogs as potential anti-malaria agents using quantitative structure– activity relationships and ADMET approach

Purpose: To design and screen for potential anti-malaria agents based on a series of 4-aminoquinolinyl analogues.Methods: Molecular fingerprint analysis was used for molecular partitioning of training and test sets. Acquired training sets were used for CoMFA and CoMSIA model construction after good alignment was achieved. Partial least squares analysis combined with external validation were used for  model evaluation. Deep analysis of acquired contour maps was performed to summarize the substituent property requirements for further rational molecular design. Using the chosen models, activity prediction and subsequent ADMET investigation were performed to discover novel designed  compounds with the desired properties.Results: Three different set partitions for model establishment were obtained using fingerprint-based selection. Partition 02 offered an optimal CoMFA model (r2 = 0.964, q2 = 0.605 and r2pred = 0.6362) and the best CoMSIA model (r2 = 0.955, q2 = 0.585 and r2 pred = 0.6403). Based on contour map analysis, a series of compounds were designed for activity prediction. Two of the compounds (wmx09, wmx25) were chosen for their ideal predicted biological activities. Subsequent ADMET investigation indicated that these compoundss have acceptable drug-like characteristics.Conclusion: The screening reveals that compounds wmx09 and wmx25 have strong potential as antimalaria agents. Keywords: Malaria, 4-Aminoquinolinyl, Molecular fingerprint, QSAR, ADME

Cheminformatics Analysis of Assertions Mined from Literature That Describe Drug-Induced Liver Injury in Different Species

Drug Induced Liver Injury (DILI) is one of the main causes of drug attrition. The ability to predict the liver effects of drug candidates from their chemical structure is critical to help guiding experimental drug discovery projects towards safer medicines. In this study, we have compiled a dataset of 951 compounds reported to produce a wide range of effects in the liver in different species, comprising humans, rodents, and non-rodents. The liver effects for this dataset were obtained as assertional meta-data, generated from MEDLINE abstracts using a unique combination of lexical and linguistic methods and ontological rules. We have analyzed this dataset using conventional cheminformatics approaches and addressed several questions pertaining to cross-species concordance of liver effects, chemical determinants of liver effects in humans, and the prediction of whether a given compound is likely to cause a liver effect in humans. We found that the concordance of liver effects was relatively low (ca. 39–44%) between different species raising the possibility that species specificity could depend on specific features of chemical structure. Compounds were clustered by their chemical similarity, and similar compounds were examined for the expected similarity of their species-dependent liver effect profiles. In most cases, similar profiles were observed for members of the same cluster, but some compounds appeared as outliers. The outliers were the subject of focused assertion re-generation from MEDLINE, as well as other data sources. In some cases, additional biological assertions were identified which were in line with expectations based on compounds' chemical similarity. The assertions were further converted to binary annotations of underlying chemicals (i.e., liver effect vs. no liver effect), and binary QSAR models were generated to predict whether a compound would be expected to produce liver effects in humans. Despite the apparent heterogeneity of data, models have shown good predictive power assessed by external five-fold cross validation procedures. The external predictive power of binary QSAR models was further confirmed by their application to compounds that were retrieved or studied after the model was developed. To the best of our knowledge, this is the first study for chemical toxicity prediction that applied QSAR modeling and other cheminformatics techniques to observational data generated by the means of automated text mining with limited manual curation, opening up new opportunities for generating and modeling chemical toxicology data

Toward predictive models for drug-induced liver injury in humans: are we there yet?

Drug-induced liver injury (DILI) is a frequent cause for the termination of drug development programs and a leading reason of drug withdrawal from the marketplace. Unfortunately, the current preclinical testing strategies, including the regulatory-required animal toxicity studies or simple in vitro tests, are insufficiently powered to predict DILI in patients reliably. Notably, the limited predictive power of such testing strategies is mostly attributed to the complex nature of DILI, a poor understanding of its mechanism, a scarcity of human hepatotoxicity data and inadequate bioinformatics capabilities. With the advent of high-content screening assays, toxicogenomics and bioinformatics, multiple end points can be studied simultaneously to improve prediction of clinically relevant DILIs. This review focuses on the current state of efforts in developing predictive models from diverse data sources for potential use in detecting human hepatotoxicity, and also aims to provide perspectives on how to further improve DILI prediction

Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction

Idiosyncratic drug-induced liver injury (iDILI) encompasses the unexpected harms that pre- scription and non-prescription drugs, herbal and dietary supplements can cause to the liver. iDILI remains a major public health problem and a major cause of drug attrition. Given the lack of biomarkers for iDILI prediction, diagnosis and prognosis, searching new models to predict and study mechanisms of iDILI is necessary. One of the major limitations of iDILI preclinical assessment has been the lack of correlation between the markers of hepatotoxicity in animal toxicological studies and clinically significant iDILI. Thus, major advances in the understanding of iDILI susceptibility and pathogenesis have come from the study of well-phenotyped iDILI patients. However, there are many gaps for explaining all the complexity of iDILI susceptibility and mechanisms. Therefore, there is a need to optimize preclinical hu- man in vitro models to reduce the risk of iDILI during drug development. Here, the current experimental models and the future directions in iDILI modelling are thoroughly discussed, focusing on the human cellular models available to study the pathophysiological mechanisms of the disease and the most used in vivo animal iDILI models. We also comment about in silico approaches and the increasing relevance of patient-derived cellular models.This work was supported by grants of Instituto de Salud Carlos III cofounded by Fondo Europeo de Desarrollo Regional-FEDER (contract numbers: PI18/01804, PI19-00883, PT20/00127, UMA18-FEDERJA-194, PY18-3364, Spain) and grants of Consejería de Salud de Andalucía cofounded by FEDER (contract number: PEMP-0127-2020, Spain). M.V.P. holds a Sara Borrell (CD21/00198, Spain) research contract from ISCIII and Consejería de Salud de Andalucía. C.L.G. holds a Juan de la Cierva Incorporación (IJCI-2017-31466, Spain) research contract from Ministerio de Ciencia del Gobierno de España. SCReN and CIBERehd are funded by ISCIII (Spain). This publication is based upon work from COST Action “CA17112dProspective European Drug-Induced Liver Injury Network” supported by COST (European Cooperation in Science and Technology); www.cost.eu. The figures in this review were created with Biorender.com

Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction

Idiosyncratic drug-induced liver injury (iDILI) encompasses the unexpected harms that prescription and non-prescription drugs, herbal and dietary supplements can cause to the liver. iDILI remains a major public health problem and a major cause of drug attrition. Given the lack of biomarkers for iDILI prediction, diagnosis and prognosis, searching new models to predict and study mechanisms of iDILI is necessary. One of the major limitations of iDILI preclinical assessment has been the lack of correlation between the markers of hepatotoxicity in animal toxicological studies and clinically significant iDILI. Thus, major advances in the understanding of iDILI susceptibility and pathogenesis have come from the study of well-phenotyped iDILI patients. However, there are many gaps for explaining all the complexity of iDILI susceptibility and mechanisms. Therefore, there is a need to optimize preclinical human in vitro models to reduce the risk of iDILI during drug development. Here, the current experimental models and the future directions in iDILI modelling are thoroughly discussed, focusing on the human cellular models available to study the pathophysiological mechanisms of the disease and the most used in vivo animal iDILI models. We also comment about in silico approaches and the increasing relevance of patient-derived cellular models.This work was supported by grants of Instituto de Salud Carlos III cofounded by Fondo Europeo de Desarrollo Regional-FEDER (contract numbers: PI18/01804, PI19-00883, PT20/00127, 3714 Antonio Segovia-Zafra et al. UMA18-FEDERJA-194, PY18-3364, Spain) and grants of Consejeríaa de Salud de Andalucı ́a cofounded by FEDER (contractnumber: PEMP-0127-2020, Spain). M.V.P. holds a Sara Borrell (CD21/00198, Spain) research contract from ISCIII and Consejerí a de Salud de Andalucía. C.L.G. holds a Juan de la Cierva Incorporación (IJCI-2017-31466, Spain) research contract from Ministerio de Ciencia del Gobierno de España. SCReN and CIBERehd are funded by ISCIII (Spain). This publication is based upon work from COST Action “CA17112dProspective European Drug-Induced Liver Injury Network” supported by COST (European Cooperation in Science and Technology)Ye

Biophysical insights into dietary antioxidants as cyclooxygenase modulators

Non-steroidal anti-inflammatory drugs (NSAIDs) are among the most widely used therapeutic agents around the world, commonly used to reduce pain. These work by targeting cyclooxygenase (COX) enzymes, which are responsible for the production of inflammatory mediators. There are adverse effects with the use of NSAIDs, including gastrointestinal bleeding, renal disease, and cardiovascular effects. Hence, there has been a rise in the development of alternatives to traditional NSAIDs. Olive oil is a main component of the Mediterranean diet, and is reputable as part of a healthy lifestyle. Phenolic compounds derived from Olea europaea contribute to the antioxidant, anti-microbial, and anti-inflammatory properties of extra virgin olive oil. However, specific mechanisms of action are not yet clear. A previous study found that oleocanthal (OLEO), a phenolic compound derived from the olive, had similar effects to ibuprofen, a commonly used NSAID. There are a multitude of additional compounds in the olive that have yet to be investigated. In this project, it was sought to identify potential olive derived compounds with the ability to inhibit COX enzymes to be used in anti-inflammatory therapeutics. The mechanisms of COX inhibition were also studied using in silico approaches. Following a literature review on COX proteins and olive compounds in Chapter 1, a description of computational theory surrounding the in silico methods employed in this thesis are presented in Chapter 2. In Chapter 3, a comprehensive literature search was performed to create a library of olive compounds, focussing on the class of phenolics for the purpose of this project. The structure of human COX-1 was constructed using homology modelling methods in Chapter 4, followed by virtual screening of the olive phenolic library using molecular docking to determine the COX inhibitory potential of all identified ligands. From the docking study, it was determined that 1-oleyltyrosol (1OL) and ligstroside derivative 2 (LG2) demonstrated the greatest binding affinity to both COX-1 and COX-2. Further screening of the compound library was performed by analysing their biological availability in Chapter 5. From examination of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of the library, a novel phenolic compound—methyl malate-β-hydoxytyrosol ester (MMHTE)—was found to both fulfil ADMET criteria and demonstrate strong binding to COX-1 and COX-2. These phenolic compounds were selected for further analysis using molecular dynamics simulations. To complement the ADMET data, a preliminary study on membrane permeability was performed. This was conducted using steered MD simulations of these compounds through a 1,2-dioleyl-sn-glycero-3-phosphocholine (DOPC) lipid bilayer, followed by umbrella sampling simulations of OLEO and MMHTE to estimate the free energy of membrane permeation. Chapter 6 presents a detailed study on the mechanisms of COX inhibition by these selected compounds using MD simulations. Classical MD simulations were carried out on COX-1 and COX-2 complexed with 1OL, LG2, OLEO, MMHTE, as well as their native ligands that were present in the crystal structure. The stability and backbone fluctuation of these complexes were determined. Protein dynamics were examined using essential dynamics methods and network analysis, which identified that the N-terminal epidermal growth factor-like domain and membrane bound domains of COX-1 and -2 exhibited altered motions when ligands were bound. Distinct dynamical modules were identified, as well as the finding that COX-2 inter-residue communications were more sensitive to ligand binding compared to COX-1. The residue contributions to binding free energy were computed using Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PPBSA) methods. Through this research, novel olive phenolic compounds were identified which may possess COX inhibitory properties. Future work may provide additional details of the mechanism of COX inhibition, as well as the synthesis of these novel compounds for in vitro and in vivo validation. Furthermore, it may be demonstrated that olive-derived compounds present a possible avenue for the development of more effective and safe therapeutics in inflammation, as well as provide mechanisms for the anti-inflammatory effects of low dosage dietary COX inhibitors

A Knowledge Based Approach of Toxicity Prediction for Drug Formulation. Modelling Drug Vehicle Relationships Using Soft Computing Techniques

This multidisciplinary thesis is concerned with the prediction of drug formulations for the reduction of drug toxicity. Both scientific and computational approaches are utilised to make original contributions to the field of predictive toxicology. The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied and reported in the literature. Furthermore, a review of the current technologies available for formulating drugs for toxicity reduction is provided. Examples of studies reported in the literature that have used these technologies to reduce drug toxicity are also reported. The thesis also provides an overview of the computational approaches currently employed in the field of in silico predictive toxicology. This overview focuses on the machine learning approaches used to build predictive QSAR classification models, with examples discovered from the literature provided. Two methodologies have been developed as part of the main work of this thesis. The first is focused on use of directed bipartite graphs and Venn diagrams for the visualisation and extraction of drug-vehicle relationships from large un-curated datasets which show changes in the patterns of toxicity. These relationships can be rapidly extracted and visualised using the methodology proposed in chapter 4. The second methodology proposed, involves mining large datasets for the extraction of drug-vehicle toxicity data. The methodology uses an area-under-the-curve principle to make pairwise comparisons of vehicles which are classified according to the toxicity protection they offer, from which predictive classification models based on random forests and decisions trees are built. The results of this methodology are reported in chapter 6

A systems toxicology framework for improving the identification of paracetamol overdose

Paracetamol (APAP) overdose is the leading cause of acute liver failure and a concerning global health issue. However, the current clinical treatment framework is heavily criticized for its sub-optimality. Within this thesis, a systems toxicology approach is taken in an attempt to provide further insight into the APAP overdose problem, and propose potential improvements to the current treatment framework. In Chapter 2, a proof-of-concept pre-clinical pharmacokinetic-pharmacodynamic (PKPD) model describing APAP metabolism and corresponding toxicity biomarkers (ALT, HMGB1, full K18, fragmented K18) is defined. A statistical model is combined with the PKPD framework to simulate in silico population groups with the aim of predicting initial APAP dose, time since overdose, and probability of liver injury. In chapter 3, an identifiability analysis is performed on the PKPD model to identify parameter uncertainties. The model is also extended, enabling predictions for individuals deemed both “healthy” and “high-risk”. In 2017 I was awarded the in vitro toxicology society mini-fellowship award, which funded 4 weeks of training in experimental wet-lab techniques. The training was used to investigate the effects of various combinations of APAP and its antidote, N’Acetylcysteine (NAC), on in vitro hepatocyte functionality. Subsequently, in chapter 4, the effect of the antidote (NAC) is incorporated into the PKPD model structure, and an additional toxicity measure is defined, describing severe loss of cell functionality. Different NAC regimens are tested, investigating their effect on both of our proposed toxicity measures. Through collaboration with the Royal Infirmary, Edinburgh, we obtained access to a clinical dataset of approximately 3,600 APAP overdose patients. In Chapter 5, a population-pharmacokinetic (Pop-PK) APAP model is defined, with PK parameters optimised based on this dataset. The framework has the ability to account for random inter-individual differences in PK parameter values. Current clinical toxicity thresholds are investigated and compared to those proposed by our model for various demographic groups