In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.

Extending the limitations in the prediction of PAMPA permeability with machine learning algorithms

Gastrointestinal absorption is a key factor amongst the ADME-related (absorption, distribution, metabolism and excretion) pharmacokinetic properties; therefore, it has a major role in drug discovery and drug safety determinations. The Parallel Artificial Membrane Permeability Assay (PAMPA) can be considered as the most popular and well-known screening assay for the measurement of gastrointestinal absorption. Our study provides quantitative structure-property relationship (QSPR) models based on experimental PAMPA permeability data for almost four hundred diverse molecules, which is a great extension of the applicability of the models in the chemical space. Two- and three-dimensional molecular descriptors were applied for the model building in every case. We have compared the performance of a classical partial least squares regression (PLS) model with two major machine learning algorithms: artificial neural networks (ANN) and support vector machine (SVM). Due to the applied gradient pH in the experiments, we have calculated the descriptors for the model building at pH values of 7.4 and 6.5, and compared the effect of pH on the performance of the models. After a complex validation protocol, the best model had an R2=0.91 for the training set, and R2= 0.84 for the external test set. The developed models are capable for the robust and fast prediction of new compounds with an excellent accuracy compared to the previous QSPR models.

Formulation and evaluation of magnesium sulphate nanoparticles for improved CNS penetrability

Magnesium (Mg²⁺) is the fourth most abundant cation in the human body and is involved in maintaining varieties of cellular and neurological functions. Magnesium deficiency has been associated with numerous diseases, particularly neurological disorders, and its supplementation has proven beneficial. However, magnesium therapy in neurological diseases is limited because of the inability of magnesium to cross the blood-brain barrier (BBB). The present study focuses on developing magnesium sulphate nanoparticles (MGSN) to improve blood-brain barrier permeability. MGSN was prepared by precipitation technique with probe sonication. The developed formulation was characterized by DLS, EDAX, FT-IR and quantitative and qualitative estimation of magnesium. According to the DLS report, the average size of the prepared MGSN is found to be 247 nm. The haemocompatibility assay studies revealed that the prepared MGSN are biocompatible at different concentrations. The in vitro BBB permeability assay conducted by Parallel Artificial Membrane Permeability Assay (PAMPA) using rat brain tissue revealed that the prepared MGSN exhibited enhanced BBB permeability as compared to the marketed i.v. MgSO₄ injection. The reversal effect of MGSN to digoxin-induced Na⁺/K⁺ ATPase enzyme inhibition using brain microslices confirmed that MGSN could attenuate the altered levels of Na⁺ and K⁺ and is useful in treating neurological diseases with altered expression of Na⁺/K⁺ ATPase activity.

Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2

Responding to the pandemic caused by SARS-CoV-2, the scientific community intensified efforts to provide drugs effective against the virus. To strengthen these efforts, the "COVID Moonshot" project has been accepting public suggestions for computationally triaged, synthesized, and tested molecules. The project aimed to identify molecules of low molecular weight with activity against the virus, for oral treatment. The ability of a drug to cross the intestinal cell membranes and enter circulation decisively influences its bioavailability, and hence the need to optimize permeability in the early stages of drug discovery. In our present work, as a contribution to the ongoing scientific efforts, we employed artificial neural network algorithms to develop QSAR tools for modelling the PAMPA effective permeability (passive diffusion) of orally administered drugs. We identified a set of 61 features most relevant in explaining drug cell permeability and used them to develop a stacked regression ensemble model, subsequently used to predict the permeability of molecules included in datasets made available through the COVID Moonshot project. Our model was shown to be robust and may provide a promising framework for predicting the potential permeability of molecules not yet synthesized, thus guiding the process of drug design.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13721-023-00410-9.

β Boswellic Acid Blocks Articular Innate Immune Responses: An In Silico and In Vitro Approach to Traditional Medicine

Osteoarthritis (OA) is hallmarked as a silent progressive rheumatic disease of the whole joint. The accumulation of inflammatory and catabolic factors such as IL6, TNFα, and COX2 drives the OA pathophysiology into cartilage degradation, synovia inflammation, and bone destruction. There is no clinical available OA treatment. Although traditional ayurvedic medicine has been using Boswellia serrata extracts (BSE) as an antirheumatic treatment for a millennium, none of the BSE components have been clinically approved. Recently, β boswellic acid (BBA) has been shown to reduce in vivo OA-cartilage loss through an unknown mechanism. We used computational pharmacology, proteomics, transcriptomics, and metabolomics to present solid evidence of BBA therapeutic properties in mouse and primary human OA joint cells. Specifically, BBA binds to the innate immune receptor Toll-like Receptor 4 (TLR4) complex and inhibits both TLR4 and Interleukin 1 Receptor (IL1R) signaling in OA chondrocytes, osteoblasts, and synoviocytes. Moreover, BBA inhibition of TLR4/IL1R downregulated reactive oxygen species (ROS) synthesis and MAPK p38/NFκB, NLRP3, IFNαβ, TNF, and ECM-related pathways. Altogether, we present a solid bulk of evidence that BBA blocks OA innate immune responses and could be transferred into the clinic as an alimentary supplement or as a therapeutic tool after clinical trial evaluations.

In silico model-based exploration of the applicability of parallel artificial membrane permeability assay (PAMPA) to screen chemicals of environmental concern

Parallel Artificial Membrane Permeability Assay (PAMPA) is an in vitro laboratory method for screening the transmembrane permeability of chemicals. Stemming from medicinal chemistry, PAMPA has the potential for use in the cost-effective high-throughput evaluation of chemicals of environmental concern. However, many chemicals of environmental concern differ substantially from pharmaceuticals in hydrophobicity and volatility. Here, we develop an in silico mass balance model to explore the impacts of chemical properties on chemical mass transfer in PAMPA and PAMPA's applicability to hydrophobic or volatile chemicals of environmental concern. The model's performance is evaluated by agreement between predicted and measured permeabilities of 1383 chemicals. The model predicts that the PAMPA measured permeability can be highly uncertain for hydrophobic chemicals because of considerable retention by the artificial membrane and for volatile chemicals because of substantial volatilization to the headspace. Notably, the permeabilities of hydrophobic chemicals are remarkably sensitive to specific experimental conditions, for example, the frequency of stirring and incubation time, challenging the comparison between measurements under different conditions. For hydrophobic chemicals, the PAMPA measured permeability may largely indicate the permeability of the unstirred water layer over the membrane, instead of the "intrinsic" permeability of the membrane, and therefore, may not be of interest for environmental exposure and risk assessments. The model also predicts that the time for mass transfer of highly hydrophobic chemicals to reach the steady state likely exceeds the incubation time, which violates the steady-state assumption used in calculating permeability from measured concentrations. Overall, our theoretical analysis underscores the importance to consider chemical properties when applying the current design of PAMPA to chemicals of environmental concern.

Application of multi-objective optimization in the study of anti-breast cancer candidate drugs

In the development of anti-breast cancer drugs, the quantitative structure-activity relationship model of compounds is usually used to select potential active compounds. However, the existing methods often have problems such as low model prediction performance, lack of overall consideration of the biological activity and related properties of compounds, and difficulty in directly selection candidate drugs. Therefore, this paper constructs a complete set of compound selection framework from three aspects: feature selection, relationship mapping and multi-objective optimization problem solving. In feature selection part, a feature selection method based on unsupervised spectral clustering is proposed. The selected features have more comprehensive information expression ability. In the relationship mapping part, a variety of machine learning algorithms are used for comparative experiments. Finally, the CatBoost algorithm is selected to perform the relationship mapping between each other, and better prediction performance is achieved. In the multi-objective optimization part, based on the analysis of the conflict relationship between the objectives, the AGE-MOEA algorithm is improved and used to solve this problem. Compared with various algorithms, the improved algorithm has better search performance.

Investigating the uncertainty of prediction accuracy for the application of physiologically based pharmacokinetic models to animal-free risk assessment of cosmetic ingredients

Physiologically based pharmacokinetic (PBPK) models are considered useful tools in animal-free risk assessment. To utilize PBPK models for risk assessment, it is necessary to compare their reliability with in vivo data. However, obtaining in vivo pharmacokinetics data for cosmetic ingredients is difficult, complicating the utilization of PBPK models for risk assessment. In this study, to utilize PBPK models for risk assessment without accuracy evaluation, we proposed a novel concept-the modeling uncertainty factor (MUF). By calculating the prediction accuracy for 150 compounds, we established that using in vitro data for metabolism-related parameters and limiting the applicability domain increase the prediction accuracy of a PBPK model. Based on the 97.5th percentile of prediction accuracy, MUF was defined at 10 for the area under the plasma concentration curve and 6 for C_max. A case study on animal-free risk assessment was conducted for bisphenol A using these MUFs. As this study was conducted mainly on pharmaceuticals, further investigation using cosmetic ingredients is pivotal. However, since internal exposure is essential in realizing animal-free risk assessment, our concept will serve as a useful tool to predict plasma concentrations without using in vivo data.

Investigation of the Uptake and Transport of Aspirin Eugenol Ester in the Caco-2 Cell Model

Background: Aspirin eugenol ester (AEE) is a novel medicinal compound synthesized by esterification of aspirin with eugenol using the prodrug principle. AEE has the pharmacological activities of being anti-inflammatory, antipyretic, analgesic, anti-cardiovascular diseases, and anti-oxidative stress However, its oral bioavailability is poor, and its intestinal absorption and transport characteristics are still unknown.

Objective: The purpose of this study was to investigate the uptake and transport mechanisms of AEE in Caco-2 cells.

Methods: The effects of time, concentration, and temperature on the transport and uptake of AEE were studied.

Results: The results showed that a higher concentration of salicylic acid (SA) was detected in the supernatant of cell lysates and cell culture medium, while AEE was not detected. Therefore, the content change of AEE was expressed as the content change of its metabolite SA. In the uptake experiment, when the factors of time, concentration, and temperature were examined, the uptake of SA reached the maximum level within 30 min, and there was concentration dependence. In addition, low temperature (4°C) could significantly reduce the uptake of SA in Caco-2 cells. In the transport experiment, under the consideration of time, concentration, and temperature, the transepithelial transport of SA from AP-BL and BL-AP sides was time-dependent. The amount of SA transported in Caco-2 cells increased with the increase of concentration, but the transmembrane transport rate had no correlation with the concentration. This phenomenon may be due to the saturation phenomenon of high concentration. The efflux ratio (ER) was less than 1, which indicated that their intestinal transport mechanism was passive transport. Moreover, the temperature had a significant effect on the transport of AEE.

Conclusion: In summary, intestinal absorption of AEE through Caco-2 cell monolayers was related to passive transport. The uptake and transport of AEE were concentration-dependent, and temperature significantly affected their uptake and transport. The absorption and transport characteristics of AEE may contribute to the exploration of mechanisms of absorption and transport of chemosynthetic drugs in vitro.

In Silico Prediction of Skin Permeability Using a Two-QSAR Approach

Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability. This investigation adopted a novel two-QSAR scheme by collectively incorporating machine learning-based hierarchical support vector regression (HSVR) and classical partial least square (PLS) to predict the skin permeability coefficient and to uncover the intrinsic permeation mechanism, respectively, based on ex vivo excised human skin permeability data compiled from the literature. The derived HSVR model functioned better than PLS as represented by the predictive performance in the training set, test set, and outlier set in addition to various statistical estimations. HSVR also delivered consistent performance upon the application of a mock test, which purposely mimicked the real challenges. PLS, contrarily, uncovered the interpretable relevance between selected descriptors and skin permeability. Thus, the synergy between interpretable PLS and predictive HSVR models can be of great use for facilitating drug discovery and development by predicting skin permeability.

Using in vitro ADME data for lead compound selection: An emphasis on PAMPA pH 5 permeability and oral bioavailability

Membrane permeability plays an important role in oral drug absorption. Caco-2 and Madin-Darby Canine Kidney (MDCK) cell culture systems have been widely used for assessing intestinal permeability. Since most drugs are absorbed passively, Parallel Artificial Membrane Permeability Assay (PAMPA) has gained popularity as a low-cost and high-throughput method in early drug discovery when compared to high-cost, labor intensive cell-based assays. At the National Center for Advancing Translational Sciences (NCATS), PAMPA pH 5 is employed as one of the Tier I absorption, distribution, metabolism, and elimination (ADME) assays. In this study, we have developed a quantitative structure activity relationship (QSAR) model using our ∼6500 compound PAMPA pH 5 permeability dataset. Along with ensemble decision tree-based methods such as Random Forest and eXtreme Gradient Boosting, we employed deep neural network and a graph convolutional neural network to model PAMPA pH 5 permeability. The classification models trained on a balanced training set provided accuracies ranging from 71% to 78% on the external set. Of the four classifiers, the graph convolutional neural network that directly operates on molecular graphs offered the best classification performance. Additionally, an ∼85% correlation was obtained between PAMPA pH 5 permeability and in vivo oral bioavailability in mice and rats. These results suggest that data from this assay (experimental or predicted) can be used to rank-order compounds for preclinical in vivo testing with a high degree of confidence, reducing cost and attrition as well as accelerating the drug discovery process. Additionally, experimental data for 486 compounds (PubChem AID: 1645871) and the best models have been made publicly available (https://opendata.ncats.nih.gov/adme/).

Advanced Analytical Tools for the Estimation of Gut Permeability of Compounds of Pharmaceutical Interest

The present study aims at developing a quantitative structure–activity relationship (QSAR) model for the determination of gut permeability of 228 pharmacological drugs at different pH conditions (3, 5, 7.4, 9, intrinsic). As a consequence, five different datasets (according to the diverse permeability shown by the compounds at the different pH values) were handled, with the aim of discriminating compounds as low-permeable or high-permeable. In order to achieve this goal, molecular descriptors for all the investigated compounds were computed and then classification models calculated by means of partial least squares discriminant analysis (PLS-DA). A high predictive capability was achieved for all models, providing correct classification rates in external validation between 80% and 96%. In order to test whether a reduction in the molecular descriptors would improve predictions and provide information about the most relevant variables, a feature selection approach, covariance selection, was used to select the most relevant subsets of predictors. This led to a slight improvement in the predictive accuracies, and it has indicated that the most relevant descriptors for the discrimination of the investigated compounds into low- and high-permeable were associated with the 2D and 3D structures.

Revisiting techniques to evaluate drug permeation through skin

INTRODUCTION

Investigating the transportation of a drug molecule through various layers of skin and determining the amount of drug retention in skin layers is of prime importance in transdermal and topical drug delivery. The information regarding drug permeation and retention in skin layers aids in optimizing a formulation and provides insight into the therapeutic efficacy of a formulation.

AREAS COVERED

This perspective covers various methods that have been explored to estimate drug/therapeutics in skin layers using in vitro, ex vivo, and in vivo conditions. In vitro methods such as diffusion techniques, ex vivo methods such as isolated perfused skin models and in vivo techniques including dermato-pharmacokinetics employing tape stripping, and microdialysis are discussed. Application of all techniques at various stages of formulation development where various local and systemic effects need to be considered.

EXPERT OPINION

The void in the existing methodologies necessitates improvement in the field of dermatologic research. Standardization of protocols, experimental setups, regulatory guidelines, and further research provides information to select an alternative for human skin to perform skin permeation experiments to increase the reliability of data generated through the available techniques. There is a need to utilize multiple techniques for appropriate dermato-pharmacokinetics evaluation and formulation's efficacy.

Design and development of topical liposomal formulations in a regulatory perspective

The skin is the absorption site for drug substances intended to treat loco-regional diseases, although its barrier properties limit the permeation of drug molecules. The growing knowledge of the skin structure and its physiology have supported the design of innovative nanosystems (e.g. liposomal systems) to improve the absorption of poorly skin-permeable drugs. However, despite the dozens of clinical trials started, few topically applied liposomal systems have been authorized both in the EU and the USA. Indeed, the intrinsic complexity of the topically applied liposomal systems, the higher production costs, the lack of standardized methods and the more stringent guidelines for assessing their benefit/risk balance can be seen as causes of such inefficient translation. The present work aimed to provide an overview of the physicochemical and biopharmaceutical characterization methods that can be applied to topical liposomal systems intended to be marketed as medicinal products, and the current regulatory provisions. The discussion highlights how such methodologies can be relevant for defining the critical quality attributes of the final product, and they can be usefully applied based on the phase of the life cycle of a liposomal product: to guide the formulation studies in the early stages of development, to rationally design preclinical and clinical trials, to support the pharmaceutical quality control system and to sustain post-marketing variations. The provided information can help define harmonized quality standards able to overcome the case-by-case approach currently applied by regulatory agencies in assessing the benefit/risk of the topically applied liposomal systems.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Fluoroquinolone-Transition Metal Complexes: A Strategy to Overcome Bacterial Resistance

Fluoroquinolones (FQs) are antibiotics widely used in the clinical practice due to their large spectrum of action against Gram-negative and some Gram-positive bacteria. Nevertheless, the misuse and overuse of these antibiotics has triggered the development of bacterial resistance mechanisms. One of the strategies to circumvent this problem is the complexation of FQs with transition metal ions, known as metalloantibiotics, which can promote different activity and enhanced pharmacological behaviour. Here, we discuss the stability of FQ metalloantibiotics and their possible translocation pathways. The main goal of the present review is to frame the present knowledge on the conjunction of biophysical and biological tools that can help to unravel the antibacterial action of FQ metalloantibiotics. An additional goal is to shed light on the studies that must be accomplished to ensure stability and viability of such metalloantibiotics. Potentiometric, spectroscopic, microscopic, microbiological, and computational techniques are surveyed. Stability and partition constants, interaction with membrane porins and elucidation of their role in the influx, determination of the antimicrobial activity against multidrug-resistant (MDR) clinical isolates, elucidation of the mechanism of action, and toxicity assays are described for FQ metalloantibiotics.

Validating ADME QSAR Models Using Marketed Drugs

Problems with drug ADME are responsible for many clinical failures. By understanding the ADME properties of marketed drugs and modeling how chemical structure contributes to these inherent properties, we can help new projects reduce their risk profiles. Kinetic aqueous solubility, the parallel artificial membrane permeability assay (PAMPA), and rat liver microsomal stability constitute the Tier I ADME assays at the National Center for Advancing Translational Sciences (NCATS). Using recent data generated from in-house lead optimization Tier I studies, we update quantitative structure-activity relationship (QSAR) models for these three endpoints and validate in silico performance against a set of marketed drugs (balanced accuracies range between 71% and 85%). Improved models and experimental datasets are of direct relevance to drug discovery projects and, together with the prediction services that have been made publicly available at the ADME@NCATS web portal (https://opendata.ncats.nih.gov/adme/), provide important tools for the drug discovery community. The results are discussed in light of our previously reported ADME models and state-of-the-art models from scientific literature.Graphical Abstract[Figure: see text].

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Predicting Pharmacokinetic Properties of Potential Anticancer Agents via Their Chromatographic Behavior on Different Reversed Phase Materials

The Quantitative Structure-Activity Relationship (QSAR) methodology was used to predict biological properties, i.e., the blood–brain distribution (log BB), fraction unbounded in the brain (f_u,brain), water-skin permeation (log K_p), binding to human plasma proteins (log K_a,HSA), and intestinal permeability (Caco-2), for three classes of fused azaisocytosine-containing congeners that were considered and tested as promising drug candidates. The compounds were characterized by lipophilic, structural, and electronic descriptors, i.e., chromatographic retention, topological polar surface area, polarizability, and molecular weight. Different reversed-phase liquid chromatography techniques were used to determine the chromatographic lipophilicity of the compounds that were tested, i.e., micellar liquid chromatography (MLC) with the ODS-2 column and polyoxyethylene lauryl ether (Brij 35) as the effluent component, an immobilized artificial membrane (IAM) chromatography with phosphatidylcholine column (IAM.PC.DD2) and chromatography with end-capped octadecylsilyl (ODS) column using aqueous solutions of acetonitrile as the mobile phases. Using multiple linear regression, we derived the statistically significant quantitative structure-activity relationships. All these QSAR equations were validated and were found to be very good. The investigations highlight the significance and possibilities of liquid chromatographic techniques with three different reversed-phase materials and QSARs methods in predicting the pharmacokinetic properties of our important organic compounds and reducing unethical animal testing.

Assessment of Finite and Infinite Dose In Vitro Experiments in Transdermal Drug Delivery

Penetration, usually with finite dosing, provides data about the total active amount in the skin and permeation, being the most used methodology, usually with infinite dosing, leads to data about pharmacokinetic parameters. The main objective of this work is to assess if results from permeation, most of them at finite dose, may be equivalent to those from penetration usually at infinite dose. The transdermal behavior of four drugs with different physicochemical properties (diclofenac sodium, ibuprofen, lidocaine, and caffeine) was studied using penetration/finite and kinetic permeation/infinite dose systems using vertical Franz diffusion cells to determine the relationships between permeation and penetration profiles. Good correlation of these two in vitro assays is difficult to find; the influence of their dosage and the proportion of different ionized/unionized compounds due to the pH of the skin layers was demonstrated. Finite and infinite dose regimens have different applications in transdermal delivery. Each approach presents its own advantages and challenges. Pharmaceutical industries are not always clear about the method and the dose to use to determine transdermal drug delivery. Being aware that this study presents results for four actives with different physicochemical properties, it can be concluded that the permeation/infinite results could not be always extrapolated to those of penetration/finite. Differences in hydrophilicity and ionization of drugs can significantly influence the lack of equivalence between the two methodologies. Further investigations in this field are still needed to study the correlation of the two methodologies and the main properties of the drugs that should be taken into account.

Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r² = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q² = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q² = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes

One of the most challenging goals in modern pharmaceutical research is to develop models that can predict drugs’ behavior, particularly permeability in human tissues. Since the permeability is closely related to the molecular properties, numerous characteristics are necessary in order to develop a reliable predictive tool. The present study attempts to decode the permeability by correlating the apparent permeability coefficient (P_app) of 33 steroids with their properties (physicochemical and structural). The P_app of the molecules was determined by in vitro experiments and the results were plotted as Y variable on a Partial Least Squares (PLS) model, while 37 pharmacokinetic and structural properties were used as X descriptors. The developed model was subjected to internal validation and it tends to be robust with good predictive potential (R²Y = 0.902, RMSEE = 0.00265379, Q²Y = 0.722, RMSEP = 0.0077). Based on the results specific properties (logS, logP, logD, PSA and VD_ss) were proved to be more important than others in terms of drugs P_app. The models can be utilized to predict the permeability of a new candidate drug avoiding needless animal experiments, as well as time and material consuming experiments.

Alternatives to Biological Skin in Permeation Studies: Current Trends and Possibilities

: The transdermal route of drugs has received increased attention in recent years due to numerous advantages over the oral and injectable routes, such as avoidance of the hepatic metabolism, protection of drugs from the gastrointestinal tract, sustained drug delivery, and good patient compliance. The assessment of ex vivo permeation during the pharmaceutical development process helps in understanding the product quality and performance of a transdermal delivery system. Generally, excised human skin relevant to the application site or animal skin is recommended for ex vivo permeation studies. However, the limited availability of the human skin and ethical issues surrounding the use of animal skin rendered these models less attractive in the permeation study. In the last three decades, enormous efforts have been put into developing artificial membranes and 3D cultured human skin models as surrogates to the human skin. This manuscript provides an insight on the European Medicines Agency (EMA) guidelines for permeation studies and the parameters affected when using Franz diffusion cells in the permeation study. The need and possibilities for skin alternatives, such as artificially cultured human skin models, parallel artificial membrane permeability assays (PAMPA), and artificial membranes for penetration and permeation studies, are comprehensively discussed.