TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity

Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint whose molecular rationale is still largely unknown. The lack of availability of high-quality data as well as robust nontesting methods makes its understanding even more difficult. Thus, the application of new explainable alternative methods is of utmost importance, with Dev Tox being one of the most animal-intensive research themes of regulatory toxicology. Descending from TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), the present work describes TISBE (TIRESIA Improved on Structure-Based Explainability), a new public web platform implementing four fundamental advancements for in silico analyses: a three times larger dataset, a transparent XAI (explainable artificial intelligence) framework employing a fragment-based fingerprint coding, a novel consensus classifier based on five independent machine learning models, and a new applicability domain (AD) method based on a double top-down approach for better estimating the prediction reliability. The training set (TS) includes as many as 1008 chemicals annotated with experimental toxicity values. Based on a 5-fold cross-validation, a median value of 0.410 for the Matthews correlation coefficient was calculated; TISBE was very effective, with a median value of sensitivity and specificity equal to 0.984 and 0.274, respectively. TISBE was applied on two external pools made of 1484 bioactive compounds and 85 pediatric drugs taken from ChEMBL (Chemical European Molecular Biology Laboratory) and TEDDY (Task-Force in Europe for Drug Development in the Young) repositories, respectively. Notably, TISBE gives users the option to clearly spot the molecular fragments responsible for the toxicity or the safety of a given chemical query and is available for free at https://prometheus.farmacia.uniba.it/tisbe.

Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives

INTRODUCTION

The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being.

AREAS COVERED

This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies.

EXPERT OPINION

The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.

Making sense of chemical space network shows signs of criticality

Chemical space modelling has great importance in unveiling and visualising latent information, which is critical in predictive toxicology related to drug discovery process. While the use of traditional molecular descriptors and fingerprints may suffer from the so-called curse of dimensionality, complex networks are devoid of the typical drawbacks of coordinate-based representations. Herein, we use chemical space networks (CSNs) to analyse the case of the developmental toxicity (Dev Tox), which remains a challenging endpoint for the difficulty of gathering enough reliable data despite very important for the protection of the maternal and child health. Our study proved that the Dev Tox CSN has a complex non-random organisation and can thus provide a wealth of meaningful information also for predictive purposes. At a phase transition, chemical similarities highlight well-established toxicophores, such as aryl derivatives, mostly neurotoxic hydantoins, barbiturates and amino alcohols, steroids, and volatile organic compounds ether-like chemicals, which are strongly suspected of the Dev Tox onset and can thus be employed as effective alerts for prioritising chemicals before testing.

Synthesis, computational and experimental pharmacological studies for (thio)ether-triazine 5-HT6R ligands with noticeable action on AChE/BChE and chalcogen-dependent intrinsic activity in search for new class of drugs against Alzheimer's disease

Alzheimer's disease is becoming a growing problem increasing at a tremendous rate. Serotonin 5-HT6 receptors appear to be a particularly attractive target from a therapeutic perspective, due to their involvement not only in cognitive processes, but also in depression and psychosis. In this work, we present the synthesis and broad biological characterization of a new series of 18 compounds with a unique 1,3,5-triazine backbone, as potent 5-HT6 receptor ligands. The main aim of this research is to compare the biological activity of the newly synthesized sulfur derivatives with their oxygen analogues and their N-demethylated O- and S-metabolites obtained for the first time. Most of the new triazines displayed high affinity (Ki < 200 nM) and selectivity towards 5-HT6R, with respect to 5-HT2AR, 5-HT7R, and D2R, in the radioligand binding assays. For selected, active compounds crystallographic studies, functional bioassays, and ADME-Tox profile in vitro were performed. The exciting novelty is that the sulfur derivatives exhibit an agonistic mode of action contrary to all other compounds obtained to date in this chemical class herein and previously reported. Advanced computational studies indicated that this intriguing functional shift might be caused by presence of chalcogen bonds formed only by the sulfur atom. In addition, the N-demethylated derivatives have emerged highly potent antioxidants and, moreover, show a significant improvement in metabolic stability compared to the parent structures. The cholinesterase study present micromolar inhibitory AChE and BChE activity for both 5-HT6 agonist 19 and potent antagonist 5. Finally, the behavioral experiments of compound 19 demonstrated its antidepressant-like properties and slight ability to improve cognitive deficits, without inducing memory impairments by itself. Described pharmacological properties of both compounds (5 and 19) allow to give a design clue for the development of multitarget compounds with 5-HT6 (both agonist and antagonist)/AChE and/or BChE mechanism in the group of 1,3,5-triazine derivatives.

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning

The endocannabinoid system, which includes cannabinoid receptor 1 and 2 subtypes (CB1R and CB2R, respectively), is responsible for the onset of various pathologies including neurodegeneration, cancer, neuropathic and inflammatory pain, obesity, and inflammatory bowel disease. Given the high similarity of CB1R and CB2R, generating subtype-selective ligands is still an open challenge. In this work, the Cannabinoid Iterative Revaluation for Classification and Explanation (CIRCE) compound prediction platform has been generated based on explainable machine learning to support the design of selective CB1R and CB2R ligands. Multilayer classifiers were combined with Shapley value analysis to facilitate explainable predictions. In test calculations, CIRCE predictions reached ∼80% accuracy and structural features determining ligand predictions were rationalized. CIRCE was designed as a web-based prediction platform that is made freely available as a part of our study.

Short-term anti-remodeling effects of gliflozins in diabetic patients with heart failure and reduced ejection fraction: an explainable artificial intelligence approach

Introduction: Sodium-glucose cotransporter type 2 inhibitors (SGLT2i), gliflozins, play an emerging role for the treatment of heart failure with reduced left ventricular ejection fraction (HFrEF). Nevertheless, the effects of SGLT2i on ventricular remodeling and function have not been completely understood yet. Explainable artificial intelligence represents an unprecedented explorative option to clinical research in this field. Based on echocardiographic evaluations, we identified some key clinical responses to gliflozins by employing a machine learning approach. Methods: Seventy-eight consecutive diabetic outpatients followed for HFrEF were enrolled in the study. Using a random forests classification, a single subject analysis was performed to define the profile of patients treated with gliflozins. An explainability analysis using Shapley values was used to outline clinical parameters that mostly improved after gliflozin therapy and machine learning runs highlighted specific variables predictive of gliflozin response. Results: The five-fold cross-validation analyses showed that gliflozins patients can be identified with a 0.70 ± 0.03% accuracy. The most relevant parameters distinguishing gliflozins patients were Right Ventricular S'-Velocity, Left Ventricular End Systolic Diameter and E/e' ratio. In addition, low Tricuspid Annular Plane Systolic Excursion values along with high Left Ventricular End Systolic Diameter and End Diastolic Volume values were associated to lower gliflozin efficacy in terms of anti-remodeling effects. Discussion: In conclusion, a machine learning analysis on a population of diabetic patients with HFrEF showed that SGLT2i treatment improved left ventricular remodeling, left ventricular diastolic and biventricular systolic function. This cardiovascular response may be predicted by routine echocardiographic parameters, with an explainable artificial intelligence approach, suggesting a lower efficacy in case of advanced stages of cardiac remodeling.

TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity

Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the utmost importance with developmental toxicity being one of the most animal-intensive areas of regulatory toxicology. In this work, the established CAESAR (Computer Assisted Evaluation of industrial chemical Substances According to Regulations) training set made of 234 chemicals for model learning is employed. Two test sets, including as a whole 585 chemicals, were instead used for validation and generalization purposes. The proposed framework favorably compares with the state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, thus resulting in a reliable support system for developmental toxicity ensuring informativeness, uncertainty estimation, generalization, and transparency. Based on the eXtreme Gradient Boosting (XGB) algorithm, our predictive model provides easy interpretative keys based on specific molecular descriptors and structural alerts enabling one to distinguish toxic and nontoxic chemicals. Inspired by the Organisation for Economic Co-operation and Development (OECD) principles for the validation of Quantitative Structure-Activity Relationships (QSARs) for regulatory purposes, the results are summarized in a standard report in portable document format, enclosing also details concerned with a density-based model applicability domain and SHAP (SHapley Additive exPlanations) explainability, the latter particularly useful to better understand the effective roles played by molecular features. Notably, our model has been implemented in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), a free of charge web platform available at http://tiresia.uniba.it.

Virtual Reverse Screening Approach to Target Type 2 Cannabinoid Receptor

A screening pool consisting of 617710 drug-like query molecules properly filtered from the ChEMBL database was employed for a ligand-based reverse screening toward the type 2 cannabinoid receptor (CB₂) target. By using our recently developed PLATO polypharmacological web platform, 233 out of 617710 drug-like molecules were prioritized on the basis of the predicted bioactivity values, better than 0.2 μM with a probability of about 98%, toward the CB₂ target. Building on these results, the occurrence of putative CB₂-related targets was also investigated for prospective repurposing studies.

252 SHORT-TERM ANTI-REMODELING EFFECTS OF GLIFLOZINS IN DIABETIC PATIENTS WITH REDUCED EJECTION FRACTION: AN EXPLAINABLE ARTIFICIAL INTELLIGENCE APPROACH

Background

Sodium glucose cotransporter type 2 inhibitors (SGLT2i), also called gliflozins, are playing an emerging role for the treatment of heart failure with reduced left ventricle ejection fraction (HFrEF). However, the direct effects of SGLT2i on left and right ventricular remodeling and function have not been completely clarified. We therefore aimed to assess clinical response to gliflozins focusing on echocardiographic evaluation and identify any predictive factors with a machine learning approach.

Methods

Based on Random Forest, a robust and consolidated machine learning approach, we carried out a single subject analysis to evaluate to which extent patients treated with gliflozins can effectively be distinguished from patients undergoing non-gliflozins treatments. Besides, we carried out an eXplainability analysis using Shapley values to outline the clinical parameters which mostly took advantage by gliflozins. Finally, machine learning experiments were designed to highlight the presence of specific clinical patterns undermining the gliflozins effectiveness.

Results

5-fold cross-validation analyses showed that gliflozin treatment was identified with a 0.70 ± 0.03% accuracy; the most important parameters supporting such accuracy were Right Ventricle S’ Velocity (RV S’), Left Ventricle End Systolic Diameter (LVESD) and E/e’ ratio. Low Tricuspid Annular Plane Systolic Excursion (TAPSE) values along with high LVESD and End Diastolic Volume (EDV) values are likely to impair the gliflozin effectiveness.

Conclusions

Treatment with gliflozins resulted in an improvement of several echocardiographic parameters related to biventricular function and left ventricle remodeling. Several clinical parameters, as simple echocardiographic parameters, may accurately predict the cardiovascular response to gliflozins treatment.

Novel Phenothiazine/Donepezil-like Hybrids Endowed with Antioxidant Activity for a Multi-Target Approach to the Therapy of Alzheimer’s Disease

Alzheimer’s disease (AD) is a complex multi-factorial neurodegenerative disorder for which only few drugs (including donepezil, DPZ) are available as symptomatic treatments; thus, researchers are focusing on the development of innovative multi-target directed ligands (MTDLs), which could also alter the course of the disease. Among other pathological factors, oxidative stress has emerged as an important factor in AD that could affect several pathways involved in the onset and progression of the pathology. Herein, we propose a new series of hybrid molecules obtained by linking a phenothiazine moiety, known for its antioxidant properties, with N-benzylpiperidine or N-benzylpiperazine fragments, mimicking the core substructure of DPZ. The investigation of the resulting hybrids showed, in addition to their antioxidant properties, their activity against some AD-related targets, such as the inhibition of cholinesterases (both AChE and BChE) and in vitro Aβ1-40 aggregation, as well as the inhibition of the innovative target fatty acid amide hydrolase (FAAH). Furthermore, the drug-likeness properties of these compounds were assessed using cheminformatic tools. Compounds 11d and 12d showed the most interesting multi-target profiles, with all the assayed activities in the low micromolar range. In silico docking calculations supported the obtained results. Compound 13, on the other hand, while inactive in the DPPH assay, showed the best results in the in vitro antioxidant cell assays conducted on both HepG2 and SHSY-5Y cell lines. These results, paired with the low or absent cytotoxicity of these compounds at tested concentrations, allow us to aim our future research at the study of novel and effective drugs and pro-drugs with similar structural characteristics.

Evaluation of Novel Guanidino-Containing Isonipecotamide Inhibitors of Blood Coagulation Factors against SARS-CoV-2 Virus Infection

Coagulation factor Xa (fXa) and thrombin (thr) are widely expressed in pulmonary tissues, where they may catalyze, together with the transmembrane serine protease 2 (TMPRSS2), the coronaviruses spike protein (SP) cleavage and activation, thus enhancing the SP binding to ACE2 and cell infection. In this study, we evaluate in vitro the ability of approved (i.e., dabigatran and rivaroxaban) and newly synthesized isonipecotamide-based reversible inhibitors of fXa/thr (cmpds 1-3) to hinder the SARS-CoV-2 infectivity of VERO cells. Nafamostat, which is a guanidine/amidine antithrombin and antiplasmin agent, disclosed as a covalent inhibitor of TMPRSS2, was also evaluated. While dabigatran and rivaroxaban at 100 μM concentration did not show any effect on SARS-CoV-2 infection, the virus preincubation with new guanidino-containing fXa-selective inhibitors 1 and 3 did decrease viral infectivity of VERO cells at subtoxic doses. When the cells were pre-incubated with 3, a reversible nanomolar inhibitor of fXa (K_i = 15 nM) showing the best in silico docking score toward TMPRSS2 (pdb 7MEQ), the SARS-CoV-2 infectivity was completely inhibited at 100 μM (p < 0.0001), where the cytopathic effect was just about 10%. The inhibitory effects of 3 on SARS-CoV-2 infection was evident (ca. 30%) at lower concentrations (3-50 μM). The covalent TMPRSS2 and the selective inhibitor nafamostat mesylate, although showing some effect (15-20% inhibition), did not achieve statistically significant activity against SARS-CoV-2 infection in the whole range of test concentrations (3-100 μM). These findings suggest that direct inhibitors of the main serine proteases of the blood coagulation cascade may have potential in SARS-CoV-2 drug discovery. Furthermore, they prove that basic amidino-containing fXa inhibitors with a higher docking score towards TMPRSS2 may be considered hits for optimizing novel small molecules protecting guest cells from SARS-CoV-2 infection.

In Vivo Investigation of (2-Hydroxypropyl)-β-cyclodextrin-Based Formulation of Spironolactone in Aqueous Solution for Paediatric Use

Spironolactone (SPL), a potent anti-aldosterone steroidal drug used to treat several diseases in paediatric patients (e.g., hypertension, primary aldosteronism, Bartter’s syndrome, and congestive heart failure), is not available in child-friendly dosage forms, and spironolactone liquids have been reported to be unpalatable. Aiming to enhance SPL solubility in aqueous solution and overcome palatability, herein, the effects of (2-hydroxypropyl)-β-cyclodextrin (HP-β-CyD) were thoroughly investigated on solubilisation in water and on masking the unpleasant taste of SPL in vivo. Although the complexation of SPL with HP-β-CyD was demonstrated through phase solubility studies, Job’s plot, NMR and computational docking studies, our in vivo tests did not show significant effects on taste aversion. Our findings, on the one hand, suggest that the formation of an inclusion complex of SPL with HP-β-CyD itself is not necessarily a good indicator for an acceptable degree of palatability, whereas, on the other hand, they constitute the basis for investigating other cyclodextrin-based formulations of the poorly water-soluble steroidal drug, including solid dosage forms, such as spray-dried powders and orodispersible tablets.

Probing Fluorinated Motifs onto Dual AChE-MAO B Inhibitors: Rational Design, Synthesis, Biological Evaluation, and Early-ADME Studies

Bioisosteric H/F or CH₂OH/CF₂H replacement was introduced in coumarin derivatives previously characterized as dual AChE-MAO B inhibitors to probe the effects on both inhibitory potency and drug-likeness. Along with in vitro screening, we investigated early-ADME parameters related to solubility and lipophilicity (Sol_7.4, CHI_7.4, log D_7.4), oral bioavailability and central nervous system (CNS) penetration (PAMPA-HDM and PAMPA-blood-brain barrier (BBB) assays, Caco-2 bidirectional transport study), and metabolic liability (half-lives and clearance in microsomes, inhibition of CYP3A4). Both specific and nonspecific tissue toxicities were determined in SH-SY5Y and HepG2 lines, respectively. Compound 15 bearing a -CF₂H motif emerged as a water-soluble, orally bioavailable CNS-permeant potent inhibitor of both human AChE (IC₅₀ = 550 nM) and MAO B (IC₅₀ = 8.2 nM, B/A selectivity > 1200). Moreover, 15 behaved as a safe and metabolically stable neuroprotective agent, devoid of cytochrome liability.

Novel Antiproliferative Biphenyl Nicotinamide: NMR Metabolomic Study of its Effect on the MCF-7 Cell in Comparison with Cisplatin and Vinblastine

A 1H-NMR-based metabolomic study was performed on MCF-7 cell lines treated with a novel nicotinamide derivative (DT-8) in comparison with two drugs characterized by a well-established mechanism of action, namely the DNA-metalating drug cisplatin (cis-diamminedichloridoplatinum(II), CDDP) and the antimitotic drug vinblastine (vinblastine, VIN). The effects of the three compounds, each one at the concentration corresponding to the IC50 value, were investigated, with respect to the controls (K), by the 1H-NMR of cells lysates and multivariate analysis (MVA) of the spectroscopic data. Relevant differences were found in the metabolic profiles of the different treatments with respect to the controls. A large overlap of the metabolic profiles in DT-8 vs. K and VIN vs. K suggests a similar biological response and mechanism of action, significantly diverse with respect to CDDP. On the other hand, DT8 seems to act by disorganizing the mitotic spindle and ultimately blocking the cell division, through a mechanism implying methionine depletion and/or S-adenosylmethionine (SAM) limitation.

Chiral Separation, X-ray Structure, and Biological Evaluation of a Potent and Reversible Dual Binding Site AChE Inhibitor

Acetylcholinesterase (AChE) inhibitors (AChEIs) still remain the leading therapeutic options for the symptomatic treatment of cognitive deficits associated with mild-to-moderate Alzheimer's disease. The search for new AChEIs benefits from well-established knowledge of the molecular interactions of selective AChEIs, such as donepezil and related dual binding site inhibitors. Starting from a previously disclosed coumarin-based inhibitor (±)-cis-1, active as racemate in the nanomolar range toward AChE, we proceeded on a double track by (i) achieving chiral resolution of the enantiomers of 1 by HPLC and (ii) preparing two close achiral analogues of 1, i.e., compounds 4 and 6. An eudismic ratio as high as 20 was observed for the (-) enantiomer of cis-1. The X-ray crystal structure of the complex between the (-)-cis-1 eutomer (coded as MC1420) and T. californica AChE was determined at 2.8 Å, and docking calculation results suggested that the eutomer in (1R,3S) absolute configuration should be energetically more favored in binding the enzyme than the eutomer in (1S,3R) configuration. The achiral analogues 4 and 6 were less effective in inhibiting AChE compared to (±)-cis-1, but interestingly butylamide 4 emerged as a potent inhibitor of butyrylcholinesterase (BChE).

Chasing ChEs-MAO B Multi-Targeting 4-Aminomethyl-7-Benzyloxy-2H-Chromen-2-ones

A series of 4-aminomethyl-7-benzyloxy-2H-chromen-2-ones was investigated with the aim of identifying multiple inhibitors of cholinesterases (acetyl- and butyryl-, AChE and BChE) and monoamine oxidase B (MAO B) as potential anti-Alzheimer molecules. Starting from a previously reported potent MAO B inhibitor (3), we studied single-point modifications at the benzyloxy or at the basic moiety. The in vitro screening highlighted triple-acting compounds (6, 8, 9, 16, 20) showing nanomolar and selective MAO B inhibition along with IC50 against ChEs at the low micromolar level. Enzyme kinetics analysis toward AChE and docking simulations on the target enzymes were run in order to get insight into the mechanism of action and plausible binding modes.

1,2,3,4-Tetrahydroisoquinoline/2H-chromen-2-one conjugates as nanomolar P-glycoprotein inhibitors: Molecular determinants for affinity and selectivity over multidrug resistance associated protein 1

A series of coniugates bearing a 1,2,3,4-tetrahydroisoquinoline motif linked to substituted 7-hydroxy-2H-chromen-2-ones was synthesized and assayed through calcein-AM test in Madin-Darby Canine Kidney (MDCK) cells overexpressing P-glycoprotein (P-gp) and closely related multidrug resistance associated protein 1 (MRP1) to probe the interference with efflux mechanisms mediated by P-gp and MRP1, respectively. A number of substituents at C3 and C4 of coumarin nucleus along with differently sized and shaped spacers was enrolled to investigate the effects of focused structural modifications over affinity and selectivity. Linker length and flexibility played a key role in enhancing P-gp affinity as proved by the most potent P-gp modulator (3h, IC₅₀ = 70 nM). A phenyl ring within the spacer (3k, 3l, 3o) and bulkier groups (Br in 3r, Ph in 3u) at coumarin C3 led to derivatives showing nanomolar activity (160 nM < IC₅₀ < 280 nM) along with outstanding selectivity over MRP1 (SI > 350). Molecular docking calculations carried out on a human MDR1 homology model structure contributed to gain insights into the ligands' binding modes. Some compounds (3d, 3h, 3l, 3r, 3t, 3u) reversed MDR thereby restoring doxorubicin cytotoxicity when co-administered with the drug into MDCK-MDR1 cells.

Investigating Structural Requirements for the Antiproliferative Activity of Biphenyl Nicotinamides

A number of trimethoxybenzoic acid anilides, previously studied as permeability glycoprotein (P-gp) modulators, were screened with the aim of identifying new anticancer agents. One of these compounds, which showed antiproliferative activity against resistant MCF-7 cell line, was selected as the hit structure. Replacement of the trimethoxybenzoyl moiety with a nicotinoyl group, in order to overcome solubility issues, led to a new series of N-biphenyl nicotinoyl anilides, among which a nitro derivative, N-(3',5'-difluoro-3-nitro-[1,1'-biphenyl]-4-yl)nicotinamide (3), displayed antiproliferative activity against MCF-7 and MDA-MB-231 cells in the nanomolar range. The search for a bioisostere of the nitro group led to nitrile analogue N-(3-cyano-4'-fluoro-[1,1'-biphenyl]-4-yl)nicotinamide (36), which shows a strong increase in activity against MCF-7 and MDA-MB-231 cells. Compound 36 induced a dose-dependent accumulation of G₂ - and M-phase MCF-7 cell populations, and a decrease in S-phase cells. Relative to vinblastine, a well-known potent antimitotic agent, compound 36 also induced G₁ -phase arrest at low doses (20-40 nm), but did not inhibit in vitro tubulin polymerization.

A greener and efficient access to substituted four- and six-membered sulfur-bearing heterocycles

The regioselective functionalization of four- and six-membered cyclic sulfones was investigated using a lithiation/functionalization strategy.

Mind the Gap! A Journey towards Computational Toxicology

Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale. However, the predictive potential of these models presents strengths and weaknesses. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. On the other, the unconscious use of in silico methods can mislead end-users with elusive results. The focus of this review is on the basic scientific and regulatory recommendations in the derivation and application of computational models. Attention is paid to examine the interplay between computational toxicology and drug discovery and development. Avoiding the easy temptation of an overoptimistic future, we report our view on what can, or cannot, realistically be done. Indeed, studies of safety/toxicity represent a key element of chemical prioritization programs carried out by chemical industries, and primarily by pharmaceutical companies.

Searching for Multi-Targeting Neurotherapeutics against Alzheimer's: Discovery of Potent AChE-MAO B Inhibitors through the Decoration of the 2H-Chromen-2-one Structural Motif

The need for developing real disease-modifying drugs against neurodegenerative syndromes, particularly Alzheimer’s disease (AD), shifted research towards reliable drug discovery strategies to unveil clinical candidates with higher therapeutic efficacy than single-targeting drugs. By following the multi-target approach, we designed and synthesized a novel class of dual acetylcholinesterase (AChE)-monoamine oxidase B (MAO-B) inhibitors through the decoration of the 2H-chromen-2-one skeleton. Compounds bearing a propargylamine moiety at position 3 displayed the highest in vitro inhibitory activities against MAO-B. Within this series, derivative 3h emerged as the most interesting hit compound, being a moderate AChE inhibitor (IC₅₀ = 8.99 µM) and a potent and selective MAO-B inhibitor (IC₅₀ = 2.8 nM). Preliminary studies in human neuroblastoma SH-SY5Y cell lines demonstrated its low cytotoxicity and disclosed a promising neuroprotective effect at low doses (0.1 µM) under oxidative stress conditions promoted by two mitochondrial toxins (oligomycin-A and rotenone). In a Madin-Darby canine kidney (MDCK)II-MDR1 cell-based transport study, Compound 3h was able to permeate the BBB-mimicking monolayer and did not result in a glycoprotein-p (P-gp) substrate, showing an efflux ratio = 0.96, close to that of diazepam.