Development of novel in silico prediction model for drug-induced ototoxicity by using naïve Bayes classifier approach

Prospects of Electrocorticography in Neuropharmacological Studies in Small Laboratory Animals

Electrophysiological methods of research are widely used in neurobiology. To assess the bioelectrical activity of the brain in small laboratory animals, electrocorticography (ECoG) is most often used, which allows the recording of signals directly from the cerebral cortex. To date, a number of methodological approaches to the manufacture and implantation of ECoG electrodes have been proposed, the complexity of which is determined by experimental tasks and logistical capabilities. Existing methods for analyzing bioelectrical signals are used to assess the functional state of the nervous system in test animals, as well as to identify correlates of pathological changes or pharmacological effects. The review presents current areas of applications of ECoG in neuropharmacological studies in small laboratory animals. Traditionally, this method is actively used to study the antiepileptic activity of new molecules. However, the possibility of using ECoG to assess the neuroprotective activity of drugs in models of traumatic, vascular, metabolic, or neurodegenerative CNS damage remains clearly underestimated. Despite the fact that ECoG has a number of disadvantages and methodological difficulties, the recorded data can be a useful addition to traditional molecular and behavioral research methods. An analysis of the works in recent years indicates a growing interest in the method as a tool for assessing the pharmacological activity of psychoactive drugs, especially in combination with classification and prediction algorithms.

Artificial intelligence applications in allergic rhinitis diagnosis: Focus on ensemble learning

Background

The diagnosis of allergic rhinitis (AR) primarily relies on symptoms and laboratory examinations. Due to limitations in outpatient settings, certain tests such as nasal provocation tests and nasal secretion smear examinations are not routinely conducted. Although there are clear diagnostic criteria, an accurate diagnosis still requires the expertise of an experienced doctor, considering the patient's medical history and conducting examinations. However, differences in physician knowledge and limitations of examination methods can result in variations in diagnosis.

Objective

Artificial intelligence is a significant outcome of the rapid advancement in computer technology today. This study aims to present an intelligent diagnosis and detection method based on ensemble learning for AR.

Method

We conducted a study on AR cases and 7 other diseases exhibiting similar symptoms, including rhinosinusitis, chronic rhinitis, upper respiratory tract infection, etc. Clinical data, encompassing medical history, clinical symptoms, allergen detection, and imaging, was collected. To develop an effective classifier, multiple models were employed to train on the same batch of data. By utilizing ensemble learning algorithms, we obtained the final ensemble classifier known as adaptive random forest-out of bag-easy ensemble (ARF-OOBEE). In order to perform comparative experiments, we selected 5 commonly used machine learning classification algorithms: Naive Bayes, support vector machine, logistic regression, multilayer perceptron, deep forest (GC Forest), and extreme gradient boosting (XGBoost).To evaluate the prediction performance of AR samples, various parameters such as precision, sensitivity, specificity, G-mean, F1-score, and area under the curve (AUC) of the receiver operating characteristic curve were jointly employed as evaluation indicators.

Results

We compared 7 classification models, including probability models, tree models, linear models, ensemble models, and neural network models. The ensemble classification algorithms, namely ARF-OOBEE and GC Forest, outperformed the other algorithms in terms of the comprehensive classification evaluation index. The accuracy of G-mean and AUC parameters improved by nearly 2% when compared to the other algorithms. Moreover, these ensemble classifiers exhibited excellent performance in handling large-scale data and unbalanced samples.

Conclusion

The ARF-OOBEE ensemble learning model demonstrates strong generalization performance and comprehensive classification abilities, making it suitable for effective application in auxiliary AR diagnosis.

Ototoxicity: a high risk to auditory function that needs to be monitored in drug development

Hearing loss constitutes a major global health concern impacting approximately 1.5 billion people worldwide. Its incidence is undergoing a substantial surge with some projecting that by 2050, a quarter of the global population will experience varying degrees of hearing deficiency. Environmental factors such as aging, exposure to loud noise, and the intake of ototoxic medications are implicated in the onset of acquired hearing loss. Ototoxicity resulting in inner ear damage is a leading cause of acquired hearing loss worldwide. This could be minimized or avoided by early testing of hearing functions in the preclinical phase of drug development. While the assessment of ototoxicity is well defined for drug candidates in the hearing field - required for drugs that are administered by the otic route and expected to reach the middle or inner ear during clinical use - ototoxicity testing is not required for all other therapeutic areas. Unfortunately, this has resulted in more than 200 ototoxic marketed medications. The aim of this publication is to raise awareness of drug-induced ototoxicity and to formulate some recommendations based on available guidelines and own experience. Ototoxicity testing programs should be adapted to the type of therapy, its indication (targeting the ear or part of other medications classes being potentially ototoxic), and the number of assets to test. For multiple molecules and/or multiple doses, screening options are available: in vitro (otic cell assays), ex vivo (cochlear explant), and in vivo (in zebrafish). In assessing the ototoxicity of a candidate drug, it is good practice to compare its ototoxicity to that of a well-known control drug of a similar class. Screening assays provide a streamlined and rapid method to know whether a drug is generally safe for inner ear structures. Mammalian animal models provide a more detailed characterization of drug ototoxicity, with a possibility to localize and quantify the damage using functional, behavioral, and morphological read-outs. Complementary histological measures are routinely conducted notably to quantify hair cells loss with cochleogram. Ototoxicity studies can be performed in rodents (mice, rats), guinea pigs and large species. However, in undertaking, or at the very least attempting, all preclinical investigations within the same species, is crucial. This encompasses starting with pharmacokinetics and pharmacology efficacy studies and extending through to toxicity studies. In life read-outs include Auditory Brainstem Response (ABR) and Distortion Product OtoAcoustic Emissions (DPOAE) measurements that assess the activity and integrity of sensory cells and the auditory nerve, reflecting sensorineural hearing loss. Accurate, reproducible, and high throughput ABR measures are fundamental to the quality and success of these preclinical trials. As in humans, in vivo otoscopic evaluations are routinely carried out to observe the tympanic membrane and auditory canal. This is often done to detect signs of inflammation. The cochlea is a tonotopic structure. Hair cell responsiveness is position and frequency dependent, with hair cells located close to the cochlea apex transducing low frequencies and those at the base transducing high frequencies. The cochleogram aims to quantify hair cells all along the cochlea and consequently determine hair cell loss related to specific frequencies. This measure is then correlated with the ABR & DPOAE results. Ototoxicity assessments evaluate the impact of drug candidates on the auditory and vestibular systems, de-risk hearing loss and balance disorders, define a safe dose, and optimize therapeutic benefits. These types of studies can be initiated during early development of a therapeutic solution, with ABR and otoscopic evaluations. Depending on the mechanism of action of the compound, studies can include DPOAE and cochleogram. Later in the development, a GLP (Good Laboratory Practice) ototoxicity study may be required based on otic related route of administration, target, or known potential otic toxicity.

A review on in silico prediction of the environmental risks posed by pharmaceutical emerging contaminants

Computer-aided (in silico) prediction has shown good potential to support the environmental risk assessment (ERA) of pharmaceutical emerging contaminants (PECs), allowing low-cost, animal-free, high-throughput screening of multiple potential risks posed by a wide variety of pharmaceuticals in the environment based on insufficient toxicity data. This review provided recent insights regarding the application of in silico approaches in prediction for environmental risks of PECs. Based on the review of 20 included articles from 8 countries published since 2018, we found that the researchers' interest and concern in this research topic were sharply aroused since 2021. Recently, in silico approaches have been widely used for the prediction of bioaccumulation and biodegradability, lethal endpoints, developmental toxicity, mutagenicity, other eco-toxicological effects such as ototoxicity and hematological toxicity, and human health hazards of exposure to PECs. Particular attention has been given to the simultaneous discernment of multiple environmental risks and health effects of PECs based on mechanistic data of pharmaceuticals using advanced bioinformatic methods such as transcriptomic analysis and network pharmacology prediction. In silico software platforms and databases used in the included studies were diversified, and there is currently no standardized and accepted in silico model for ERA of PECs. Date suggested that in silico prediction of the environmental risks posed by PECs is still in its infancy. Considerable critical challenges need to be addressed, including consideration of environmental exposure concentration for PECs, interactions among mixtures of PECs and other contaminants coexisting in environments, and development of in silico models specific to ERA of PECs.

Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers

The processing of liquisolid systems (LSS), which are considered a promising approach to improving the oral bioavailability of poorly soluble drugs, has proven challenging due to the relatively high amount of liquid phase incorporated within them. The objective of this study was to apply machine-learning tools to better understand the effects of formulation factors and/or tableting process parameters on the flowability and compaction properties of LSS with silica-based mesoporous excipients as carriers. In addition, the results of the flowability testing and dynamic compaction analysis of liquisolid admixtures were used to build data sets and develop predictive multivariate models. In the regression analysis, six different algorithms were used to model the relationship between tensile strength (TS), the target variable, and eight other input variables. The AdaBoost algorithm provided the best-fit model for predicting TS (coefficient of determination = 0.94), with ejection stress (ES), compaction pressure, and carrier type being the parameters that influenced its performance the most. The same algorithm was best for classification (precision = 0.90), depending on the type of carrier used, with detachment stress, ES, and TS as variables affecting the performance of the model. Furthermore, the formulations with Neusilin® US2 were able to maintain good flowability and satisfactory values of TS despite having a higher liquid load compared to the other two carriers.

Curcumin attenuates gentamicin and sodium salicylate ototoxic effects by modulating the nuclear factor-kappaB and apoptotic pathways in rats

This study aimed to investigate the effectiveness of curcumin (CCM) against gentamicin (GEN) and sodium salicylates (NaS)-induced ototoxic effects in rats. For 15 consecutive days, seven rat groups were given 1 mL/rat physiological saline orally, 1 mL/rat olive oil orally, 50 mg/kg bwt CCM orally, 120 mg/kg bwt GEN intraperitoneally, 300 mg/kg bwt NaS intraperitoneally, CCM+GEN, or CCM+NaS. The distortion product otoacoustic emission measurements were conducted. The rats' hearing function and balance have been behaviorally assessed using auditory startle response, Preyer reflex, and beam balance scale tests. The serum lipid peroxidation and oxidative stress biomarkers have been measured. Immunohistochemical investigations of the apoptotic marker caspase-3 and the inflammatory indicator nuclear factor kappa (NF-κB) in cochlear tissues were conducted. GEN and NaS exposure resulted in deficit hearing and impaired ability to retain balance. GEN and NaS exposure significantly decreased the reduced glutathione level and catalase activity but increased malondialdehyde content. GEN and NaS exposure evoked pathological alterations in cochlear and vestibular tissues and increased caspase-3 and NF-κB immunoexpression. CCM significantly counteracted the GEN and NaS injurious effects. These outcomes concluded that CCM could be a naturally efficient therapeutic agent against GEN and NaS-associated ototoxic side effects.

Induced Pluripotent Stem Cells, a Stepping Stone to In Vitro Human Models of Hearing Loss

Hearing loss is the most prevalent sensorineural impairment in humans. Yet despite very active research, no effective therapy other than the cochlear implant has reached the clinic. Main reasons for this failure are the multifactorial nature of the disorder, its heterogeneity, and a late onset that hinders the identification of etiological factors. Another problem is the lack of human samples such that practically all the work has been conducted on animals. Although highly valuable data have been obtained from such models, there is the risk that inter-species differences exist that may compromise the relevance of the gathered data. Human-based models are therefore direly needed. The irruption of human induced pluripotent stem cell technologies in the field of hearing research offers the possibility to generate an array of otic cell models of human origin; these may enable the identification of guiding signalling cues during inner ear development and of the mechanisms that lead from genetic alterations to pathology. These models will also be extremely valuable when conducting ototoxicity analyses and when exploring new avenues towards regeneration in the inner ear. This review summarises some of the work that has already been conducted with these cells and contemplates future possibilities.

A brief guide to machine learning for antibiotic discovery

Rising antibiotic resistance and an alarmingly lean antibiotic pipeline require the adoption of novel approaches to rapidly discover new structural and functional classes of antibiotics. Excitingly, algorithmic approaches to antibiotic discovery are sufficiently advanced to meaningfully influence the antibiotic discovery process. Indeed, once trained on high-quality datasets, contemporary machine-learning and deep-learning models can be used to perform predictions for new antibiotics across vast chemical spaces, orders of magnitude more rapidly than compounds can be screened in the laboratory. This increases the probability of discovering new antibiotics with desirable properties. In this short review, we briefly describe the utility of contemporary machine-learning and deep-learning approaches to guide the discovery of new small-molecule antibiotics and unidentified natural products. We then propose a call to action for more open sharing of high-quality screening datasets to accelerate the rate at which forthcoming antibiotic-prediction models can be trained. Together, we aim to introduce antibiotic discoverers to a sample of recent applications of contemporary algorithmic methods to facilitate the wider adoption of these powerful computational approaches.

Development of binary classification models for assessment of drug-induced liver injury in humans using a large set of FDA-approved drugs

Drug-induced liver injury (DILI) has been identified as one of the major causes for drugs withdrawn from the market, and even termination during the late stages of development. Therefore, it is imperative to evaluate the DILI potential of lead compounds during the research and development process. Although various computational models have been developed to predict DILI, most of which applied the DILI data were extracted from preclinical sources. In this investigation, the in silico prediction models for DILI were constructed based on 1140 FDA-approved drugs by using naïve Bayes classifier approach. The genetic algorithm method was applied for the molecular descriptors selection. Among these established prediction models, the NB-11 model based on eight molecular descriptors combined with ECFP_18 showed the best prediction performance for DILI, which gave 91.7% overall prediction accuracy for the training set, and 68.9% concordance for the external test set. Therefore, the established NB-11 prediction model can be used as a reliable virtual screening tool to predict DILI adverse effect in the early stages of drug design. In addition, some new structural alters for DILI were identified, which could be used for structural optimization in the future drug design by medicinal chemists.

Machine Learning Models for Predicting Liver Toxicity

Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.

FP-ADMET: a compendium of fingerprint-based ADMET prediction models

MOTIVATION

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors.

AVAILABILITY

The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet .

Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study

In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses. Several machine learners were scanned, however, Xgboost, support vector machines (SVM) and random forests (RF) were the best with testing set accuracies reaching 90%. Three potential hits were identified upon using the method to scan known untested FDA approved drugs against TMPRSS2. Subsequent molecular dynamics simulation and covalent docking supported the results of the new computational approach.

Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings

Over 100 drugs and chemicals are associated with permanent hearing loss, tinnitus, and vestibular deficits, collectively known as ototoxicity. The ototoxic potential of drugs is rarely assessed in pre-clinical drug development or during clinical trials, so this debilitating side-effect is often discovered as patients begin to report hearing loss. Furthermore, drug-induced ototoxicity in adults, and particularly in elderly patients, may go unrecognized due to hearing loss from a variety of etiologies because of a lack of baseline assessments immediately prior to novel therapeutic treatment. During the current pandemic, there is an intense effort to identify new drugs or repurpose FDA-approved drugs to treat COVID-19. Several potential COVID-19 therapeutics are known ototoxins, including chloroquine (CQ) and lopinavir-ritonavir, demonstrating the necessity to identify ototoxic potential in existing and novel medicines. Furthermore, several factors are emerging as potentiators of ototoxicity, such as inflammation (a hallmark of COVID-19), genetic polymorphisms, and ototoxic synergy with co-therapeutics, increasing the necessity to evaluate a drug's potential to induce ototoxicity under varying conditions. Here, we review the potential of COVID-19 therapies to induce ototoxicity and factors that may compound their ototoxic effects. We then discuss two models for rapidly detecting the potential for ototoxicity: mammalian auditory cell lines and the larval zebrafish lateral line. These models offer considerable value for pre-clinical drug development, including development of COVID-19 therapies. Finally, we show the validity of in silico screening for ototoxic potential using a computational model that compares structural similarity of compounds of interest with a database of known ototoxins and non-ototoxins. Preclinical screening at in silico, in vitro, and in vivo levels can provide an earlier indication of the potential for ototoxicity and identify the subset of candidate therapeutics for treating COVID-19 that need to be monitored for ototoxicity as for other widely-used clinical therapeutics, like aminoglycosides and cisplatin.

In silico prediction of drug-induced ototoxicity using machine learning and deep learning methods

Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration of the inner ear. Herein, we focused on the in silico modeling of drug-induced ototoxicity of chemicals. We collected 1,102 ototoxic medications and 1,705 non-ototoxic drugs. Based on the data set, a series of computational models were developed with different traditional machine learning and deep learning algorithms implemented on an online chemical database and modeling environment. Six ML models performed best on 5-fold cross-validation and test set. A consensus model was developed with the best individual models. These models were further validated with an external validation. The consensus model showed best predictive ability, with high accuracy of 0.95 on test set and 0.90 on validation set. The consensus model and the data sets used for model development are available at https://ochem.eu/model/46566321. Besides, 16 structural alerts responsible for drug-induced ototoxicity were identified. We hope the results could provide meaningful knowledge and useful tools for ototoxicity evaluation in drug discovery and environmental risk assessment.

Molecular Scaffold Hopping via Holistic Molecular Representation

Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales .