Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and β-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.

Isopropyl Gallate, a Gallic Acid Derivative: In Silico and In Vitro Investigation of Its Effects on Leishmania major

Isopropyl gallate (IPG) is a polyphenol obtained from alterations in the gallic acid molecule via acid catalysis with previously reported leishmanicidal and trypanocidal activities. The present study aims to evaluate in silico binding activity towards some targets for antileishmanial chemotherapy against Leishmania major species, and ADMET parameters for IPG, as well as in vitro antileishmanial and cytotoxic effects. Molecular docking was performed using AutoDockVina and BIOVIA Discovery Studio software, whereas in silico analysis used SwissADME, PreADMET and admetSAR software. In vitro antileishmanial activity on promastigotes and amastigotes of Leishmania major, cytotoxicity and macrophages activation were assessed. IPG exhibited affinity for pteridine reductase (PTR1; -8.2 kcal/mol) and oligopeptidase B (OPB; -8.0 kcal/mol) enzymes. ADMET assays demonstrated good lipophilicity, oral bioavailability, and skin permeability, as well as non-mutagenic, non-carcinogenic properties and low risk of cardiac toxicity for IPG. Moreover, IPG inhibited the in vitro growth of promastigotes (IC₅₀ = 90.813 µM), presented significant activity against amastigotes (IC₅₀ = 13.45 μM), promoted low cytotoxicity in macrophages (CC₅₀ = 1260 μM), and increased phagocytic capacity. These results suggest IPG is more selectively toxic to the parasite than to mammalian cells. IPG demonstrated acceptable in silico pharmacokinetics parameters, and reduced infection and infectivity in parasitized macrophages, possibly involving macrophage activation pathways and inhibition of leishmania enzymes.

Design, synthesis, and evaluation of novel (E)-N'-(3-allyl-2-hydroxy)benzylidene-2-(4-oxoquinazolin-3(4H)-yl)acetohydrazides as antitumor agents

In our continuing search for novel small-molecule anticancer agents, we designed and synthesized a series of novel (E)-N'-(3-allyl-2-hydroxy)benzylidene-2-(4-oxoquinazolin-3(4H)-yl)acetohydrazides (5), focusing on the modification of substitution in the quinazolin-4(3H)-one moiety. The biological evaluation showed that all 13 designed and synthesized compounds displayed significant cytotoxicity against three human cancer cell lines (SW620, colon cancer; PC-3, prostate cancer; NCI-H23, lung cancer). The most potent compound 5l displayed cytotoxicity up to 213-fold more potent than 5-fluorouracil and 87-fold more potent than PAC-1, the first procaspase-activating compound. Structure-activity relationship analysis revealed that substitution of either electron-withdrawing or electron-releasing groups at positions 6 or 7 on the quinazolin-4(3H)-4-one moiety increased the cytotoxicity of the compounds, but substitution at position 6 seemed to be more favorable. In the caspase activation assay, compound 5l was found to activate the caspase activity by 291% in comparison to PAC-1, which was used as a control. Further docking simulation also revealed that this compound may be a potent allosteric inhibitor of procaspase-3 through chelation of the inhibitory zinc ion. Physicochemical and ADMET calculations for 5l provided useful information of its suitable absorption profile and some toxicological effects that need further optimization to be developed as a promising anticancer agent.

Global Analysis of Models for Predicting Human Absorption: QSAR, In Vitro, and Preclinical Models

Models intended to predict intestinal absorption are an essential part of the drug development process. Although many models exist for capturing intestinal absorption, many questions still exist around the applicability of these models to drug types like "beyond rule of 5" (bRo5) and low absorption compounds. This presents a challenge as current models have not been rigorously tested to understand intestinal absorption. Here, we assembled a large, structurally diverse dataset of ∼1000 compounds with known in vitro, preclinical, and human permeability and/or absorption data. In silico (quantitative structure-activity relationship), in vitro (Caco-2), and in vivo (rat) models were statistically evaluated for predictive performance against this human intestinal absorption dataset. We expect this evaluation to serve as a resource for DMPK scientists and medicinal/computational chemists to increase their understanding of permeability and absorption model utility and applications for academia and industry.

Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier

BACKGROUND

Diabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. Therefore, it is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM.

METHODS

Aiming at the problem of high-dimensional feature space and high feature redundancy of medical data, as well as the problem of data imbalance often faced. This study explored different supervised classifiers, combined with SVM-SMOTE and two feature dimensionality reduction methods (Logistic stepwise regression and LAASO) to classify the diabetes survey sample data with unbalanced categories and complex related factors. Analysis and discussion of the classification results of 4 supervised classifiers based on 4 data processing methods. Five indicators including Accuracy, Precision, Recall, F1-Score and AUC are selected as the key indicators to evaluate the performance of the classification model.

RESULTS

According to the result, Random Forest Classifier combining SVM-SMOTE resampling technology and LASSO feature screening method (Accuracy = 0.890, Precision = 0.869, Recall = 0.919, F1-Score = 0.893, AUC = 0.948) proved the best way to tell those at high risk of DM. Besides, the combined algorithm helps enhance the classification performance for prediction of high-risk people of DM. Also, age, region, heart rate, hypertension, hyperlipidemia and BMI are the top six most critical characteristic variables affecting diabetes.

CONCLUSIONS

The Random Forest Classifier combining with SVM-SMOTE and LASSO feature reduction method perform best in identifying high-risk people of DM from individuals. And the combined method proposed in the study would be a good tool for early screening of DM.

Structure-activity relationship-based chemical classification of highly imbalanced Tox21 datasets

The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F₁ score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g., > 28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.

In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling

One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.

A novel adaptive ensemble classification framework for ADME prediction

AECF is a GA based ensemble method. It includes four components which are (1) data balancing, (2) generating individual models, (3) combining individual models, and (4) optimizing the ensemble.