DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies.

MOTIVATION: The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy. RESULTS: Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.

DeepTTA: a transformer-based model for predicting cancer drug response.

Identifying new lead molecules to treat cancer requires more than a decade of dedicated effort. Before selected drug candidates are used in the clinic, their anti-cancer activity is generally validated by in vitro cellular experiments. Therefore, accurate prediction of cancer drug response is a critical and challenging task for anti-cancer drugs design and precision medicine. With the development of pharmacogenomics, the combination of efficient drug feature extraction methods and omics data has made it possible to use computational models to assist in drug response prediction. In this study, we propose DeepTTA, a novel end-to-end deep learning model that utilizes transformer for drug representation learning and a multilayer neural network for transcriptomic data prediction of the anti-cancer drug responses. Specifically, DeepTTA uses transcriptomic gene expression data and chemical substructures of drugs for drug response prediction. Compared to existing methods, DeepTTA achieved higher performance in terms of root mean square error, Pearson correlation coefficient and Spearman's rank correlation coefficient on multiple test sets. Moreover, we discovered that anti-cancer drugs bortezomib and dactinomycin provide a potential therapeutic option with multiple clinical indications. With its excellent performance, DeepTTA is expected to be an effective method in cancer drug design.

Assessment of nonlinear dose-response relationships via nonparametric regression.

We propose a simple approach to assess whether a nonlinear parametric model is appropriate to depict the dose-response relationships and whether two parametric models can be applied to fit a dataset via nonparametric regression. The proposed approach can compensate for the ANOVA, which is sometimes conservative, and is very easy to implement. We illustrate the performance by analyzing experimental examples and a small simulation study.

Exposure to Secondhand Smoke Extract Increases Cisplatin Resistance in Head and Neck Cancer Cells.

Chemotherapy and radiotherapy resistance are major obstacles in the long-term efficacy of head and neck squamous cell carcinoma (HNSCC) treatment. Secondhand smoke (SHS) exposure is common and has been proposed as an independent predictor of HNSCC recurrence and disease-free survival. However, the underlying mechanisms responsible for these negative patient outcomes are unknown. To assess the effects of SHS exposure on cisplatin efficacy in cancer cells, three distinct HNSCC cell lines were exposed to sidestream (SS) smoke, the main component of SHS, at concentrations mimicking the nicotine level seen in passive smokers' saliva and treated with cisplatin (0.01-100 µM) for 48 h. Compared to cisplatin treatment alone, cancer cells exposed to both cisplatin and SS smoke extract showed significantly lower cisplatin-induced cell death and higher cell viability, IC50, and indefinite survival capacity. However, SS smoke extract exposure alone did not change cancer cell viability, cell death, or cell proliferation compared to unexposed control cancer cells. Mechanistically, exposure to SS smoke extract significantly reduced the expression of cisplatin influx transporter CTR1, and increased the expression of multidrug-resistant proteins ABCG2 and ATP7A. Our study is the first to document that exposure to SHS can increase cisplatin resistance by altering the expression of several proteins involved in multidrug resistance, thus increasing the cells' capability to evade cisplatin-induced cell death. These findings emphasize the urgent need for clinicians to consider the potential role of SHS on treatment outcomes and to advise cancer patients and caregivers on the potential benefits of avoiding SHS exposure.

Synthesis, Computational Study, and In Vitro α-Glucosidase Inhibitory Action of Thiourea Derivatives Based on 3-Aminopyridin-2(1H)-Ones.

Reactions with allyl-, acetyl-, and phenylisothiocyanate have been studied on the basis of 3-amino-4,6-dimethylpyridine-2(1H)-one, 3-amino-4-phenylpyridine-2-one, and 3-amino-4-(thiophene-2-yl)pyridine-2(1H)-one (benzoyl-)isothiocyanates, and the corresponding thioureide derivatives 8-11a-c were obtained. Twelve thiourea derivatives were obtained and studied for their anti-diabetic activity against the enzyme α-glucosidase in comparison with the standard drug acarbose. The comparison drug acarbose inhibits the activity of α-glucosidase at a concentration of 15 mM by 46.1% (IC50 for acarbose is 11.96 mM). According to the results of the conducted studies, it was shown that alkyl and phenyl thiourea derivatives 8,9a-c, in contrast to their acetyl-(benzoyl) derivatives and 10,11a-c, show high antidiabetic activity. Thus, 1-(4,6-dimethyl-2-oxo-1,2-dihydropyridin-3-yl)-3-phenylthiourea 9a has the highest inhibitory activity against the enzyme α-glucosidase, exceeding the activity of the comparison drug acarbose, which inhibits the activity of α-glucosidase by 56.6% at a concentration of 15 mm (IC50 = 9,77 mM). 1-(6-methyl-2-oxo 4-(thiophen-2-yl)-1,2-dihydropyridin-3-yl)-3-phenylthiourea 9c has inhibitory activity against the enzyme α-glucosidase, comparable to the comparison drug acarbose, inhibiting the activity of α-glucosidase at a concentration of 15 mm per 41.2% (IC50 = 12,94 mM). Compounds 8a, 8b, and 9b showed inhibitory activity against the enzyme α-glucosidase, with a lower activity compared to acarbose, inhibiting the activity of α-glucosidase at a concentration of 15 mM by 23.3%, 26.9%, and 35.2%, respectively. The IC50 against α-glucosidase for compounds 8a, 8b, and 9b was found to be 16.64 mM, 19.79 mM, and 21.79 mM, respectively. The other compounds 8c, 10a, 10b, 10c, 11a, 11b, and 11c did not show inhibitory activity against α-glucosidase. Thus, the newly synthesized derivatives of thiourea based on 3-aminopyridine-2(1H)-ones are promising candidates for the further modification and study of their potential anti-diabetic activity. These positive bioanalytical results will stimulate further in-depth studies, including in vivo models.

A Scoping Review on Cucumis melo and Its Anti-Cancer Properties.

Cucumis melo L., better known by its popular cultivar cantaloupe, is an economically significant crop in the Cucurbitaceae family. Melon peel and seeds have shown medicinal potential due to their numerous biological qualities, including anti-inflammatory, anti-cancer, antibacterial, hepatoprotective and immunomodulatory effects to treat cardiovascular disease, diabetes and oedema. This scoping review aims to broaden the research scope on the cancer-fighting abilities of melon extract and its half maximal inhibitory concentration (IC50). Three databases which are Scopus, ScienceDirect and PubMed were used to locate relevant publications utilising the keywords 'melon', 'Cucumis melo', 'inhibitory activity', 'cancer' and 'anti-cancer'. The Preferred Reporting Items for Systematic and Meta-analyses extension for Scoping Review (PRISMA-ScR) framework was used in conducting this study. Out of 904 articles, 14 articles met the inclusion criteria and were used in this analysis. These articles were published in English between 2000 and 2023 with full text accessibility, specifically addressed the fruit cantaloupe (Cucumis melo L.) or melon and reported on any type of cancer. Cucumis melo extract showed promising anti-cancer action in both in vitro and in vivo investigations on eight different cancer types: cervical, colon, prostate, leukaemia, multiple myeloma, breast, hepatoma and ovarian cancer. A thorough analysis shows that some of the IC50 values were significantly low, especially in cases of colon and prostate cancer, indicating a significant anti-cancer effect. The substantial anti-cancer benefits of Cucumis melo fruit extracts point to the necessity for additional investigation into their potential for cancer therapy on each form of cancer.

Discovery of Isojacareubin as a covalent inhibitor of SARS-CoV-2 main protease using structural and experimental approaches.

The ongoing pandemic with the emergence of immune evasion potential and, particularly, the current omicron subvariants intensified the situation further. Although vaccines are available, the immune evasion capabilities of the recent variants demand further efficient therapeutic choices to control the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Hence, considering the necessity of the small molecule inhibitor, we target the main protease (3CLpro), which is an appealing target for the development of antiviral drugs against SARS-CoV-2. High-throughput molecular in silico screening of South African natural compounds database reported Isojacareubin and Glabranin as the potential inhibitors for the main protease. The calculated docking scores were reported to be -8.47 and -8.03 kcal/mol, respectively. Moreover, the structural dynamic assessment reported that Isojacareubin in complex with 3CLpro exhibit a more stable dynamic behavior than Glabranin. Inhibition assay indicated that Isojacareubin could inhibit SARS-CoV-2 3CLpro in a time- and dose-dependent manner, with half maximal inhibitory concentration values of 16.00 ± 1.35 µM (60 min incubation). Next, the covalent binding sites of Isojacareubin on SARS-CoV-2 3CLpro was identified by biomass spectrometry, which reported that Isojacareubin can covalently bind to thiols or Cysteine through Michael addition. To evaluate the inactivation potency of Isojacareubin, the inactivation kinetics was further investigated. The inactivation kinetic curves were plotted according to various concentrations with gradient-ascending incubation times. The KI value of Isojacareubin was determined as 30.71 µM, whereas the Kinact value was calculated as 0.054 min-1 . These results suggest that Isojacareubin is a covalent inhibitor of SARS-CoV-2 3CLpro .

A new class of CYP1B1 inhibitors derived from bentranil.

Cytochrome P450 1B1 (CYP1B1) is highly expressed in a variety of tumors and implicated to drug resistance. More and more researches have suggested that CYP1B1 is a new target for cancer prevention and therapy. Various CYP1B1 inhibitors with a rigid polycyclic skeleton have been developed, such as flavonoids, trans-stilbenes, and quinazolines. To obtain a new class of CYP1B1 inhibitors, we designed and synthesized a series of bentranil analogues, moreover, IC50 determinations were performed for CYP1B1 inhibition of five of these compounds and found that 6o and 6q were the best inhibitors, with IC50 values in the nM range. The selectivity index (SI) of CYP1B1 over CYP1A1 and CYP1A2 was 30-fold higher than that of α-naphthoflavone (ANF). The molecular docking results showed that compound 6q fitted better into the CYP1B1 binding site than other compounds, which was consistent with our experimental results. On the basis of 6o and 6q, it is expected to develop CYP1B1 inhibitors with stronger affinity, higher selectivity and better solubility.

Effects of ophthalmic surface anesthetic alcaine on the proliferation and apoptosis of human corneal endothelial cells through HIF-1α regulation.

The corneal endothelium is a monolayer, which mediates solute and water flux across the posterior corneal surface. Alcaine's main component proparacaine is paramount in human corneal endothelium (HCE) cell regulation. This study explored the mechanism of alcaine in regulating HCE cells. HCE cell morphology under gradient concentrations was observed by an optical microscope. Cell proliferation and viability were detected by MTT assay to determine the half inhibitory concentration (IC 50). Cell apoptosis rate, HIF-1α mRNA expression, and HIF-1α, p/t-JNK and Caspase-3 protein levels were detected by flow cytometry, RT-qPCR, and Western blot. After treatment with alcaine at 0.625-5 g/L concentration range for 24 h, HCE cells showed cytoplasmic vacuolation, cell shrinkage, separation from culture matrix, and eventual death. Alcaine treated-HCE cell proliferation was decreased in a dose-dependent manner. The IC 50 of alcaine was 1.26 g/L. After alcaine treatment, HCE cell apoptosis rate was promoted and HIF-1α levels in HCE cells were stimulated. Knockdown of HIF-1α partially annulled the effects of alcaine on inhibiting HCE cell proliferation and facilitating apoptosis. Alcaine might activate the JNK/caspase-3 pathway by increasing HIF-1α. The inhibition of the JNK/caspase-3 pathway partially abrogated the effects of alcaine on inhibiting HCE cell proliferation and promoting apoptosis. Alcaine might affect HCE cell proliferation and apoptosis by upregulating HIF-1α and activating the JNK/caspase-3 pathway.

A Facile Synthesis and Molecular Characterization of Certain New Anti-Proliferative Indole-Based Chemical Entities.

Cancer cells frequently develop drug resistance, which leads to chemotherapeutic treatment failure. Additionally, chemotherapies are hindered by their high toxicity. Therefore, the development of new chemotherapeutic drugs with improved clinical outcomes and low toxicity is a major priority. Several indole derivatives exhibit distinctive anti-cancer mechanisms which have been associated with various molecular targets. In this study, target compounds 4a-q were obtained through the reaction of substituted benzyl chloride with hydrazine hydrate, which produces benzyl hydrazine. Subsequently, the appropriate substituted benzyl hydrazine was allowed to react with 1H-indole-2-carboxylic acid or 5-methoxy-1H-indole-2-carboxylic acid using 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide as a coupling agent. All compounds exhibited cytotoxicity in three cell lines, namely, MCF-7, A549, and HCT. Compound 4e exhibited the highest cytotoxicity, with an average IC50 of 2 µM. Moreover, a flow cytometry study revealed a significantly increased prevalence of Annexin-V and 7-AAD positive cell populations. Several derivatives of 4a-q showed moderate to high cytotoxicity against the tested cell lines, with compound 4e having the highest cytotoxicity, indicating that it may possess potential apoptosis-inducing capabilities.

Significance of Astragaloside IV from the Roots of Astragalus mongholicus as an Acetylcholinesterase Inhibitor-From the Computational and Biomimetic Analyses to the In Vitro and In Vivo Studies of Safety.

The main aim of the study was to assess the acetylcholinesterase-inhibitory potential of triterpenoid saponins (astragalosides) found in the roots of Astragalus mongholicus. For this purpose, the TLC bioautography method was applied and then the IC50 values were calculated for astragalosides II, III and IV (5.9 µM; 4.2 µM, and 4.0 µM, respectively). Moreover, molecular dynamics simulations were carried outto assess the affinity of the tested compounds for POPC and POPG-containing lipid bilayers, which in this case are the models of the blood-brain barrier (BBB). All determined free energy profiles confirmed that astragalosides exhibit great affinity for the lipid bilayer. A good correlation was obtained when comparing the logarithm of n-octanol/water partition coefficient (logPow) lipophilicity descriptor values with the smallest values of free energy of the determined 1D profiles. The affinity for the lipid bilayers changes in the same order as the corresponding logPow values, i.e.,: I > II > III~IV. All compounds exhibit a high and also relatively similar magnitude of binding energies, varying from ca. -55 to -51 kJ/mol. Apositive correlation between the experimentally-determined IC50 values and the theoretically-predicted binding energies expressed by the correlation coefficient value equal 0.956 was observed.

Stability Analysis of the Asiatic Acid-COX-2 Complex Using 100 ns Molecular Dynamic Simulations and Its Selectivity against COX-2 as a Potential Anti-Inflammatory Candidate.

Asiatic acid, a triterpenoid compound, has been shown to have anti-inflammatory activity through the inhibition of the formation of cyclooxygenase-2 (COX-2) in vitro and in vivo. This study was conducted to determine the binding stability and the inhibitory potential of asiatic acid as an anti-inflammatory candidate. The study involved in vitro testing utilizing a colorimetric kit as well as in silico testing for the pharmacophore modeling and molecular dynamic (MD) simulation of asiatic acid against COX-2 (PDB ID: 3NT1). The MD simulations showed a stable binding of asiatic acid to COX-2 and an RMSD range of 1-1.5 Å with fluctuations at the residues of Phe41, Leu42, Ile45, Arg44, Asp367, Val550, Glu366, His246, and Gly227. The total binding energy of the asiatic acid-COX-2 complex is -7.371 kcal/mol. The anti-inflammatory activity of the asiatic acid inhibition of COX-2 was detected at IC50 values of 120.17 µM. Based on pharmacophore modeling, we discovered that carboxylate and hydroxyl are the two main functional groups that act as hydrogen bond donors and acceptors interacting with the COX-2 enzyme. From the results, it is evident that asiatic acid is a potential anti-inflammatory candidate with high inhibitory activity in relation to the COX-2 enzyme.

Disparities in Cisplatin-Induced Cytotoxicity-A Meta-Analysis of Selected Cancer Cell Lines.

Cisplatin is a classic anticancer drug widely used as a reference drug to test new metal complex drug candidates. We found an unexpected diversity in cisplatin-related cytotoxicity values, expressed as IC50 (the half-maximal inhibitory concentration) in tumour cell lines, such as MCF-7, HepG2 and HeLa. We reviewed the data published from 2018 to 2022. A total of 41 articles based on 56 in vitro experiments met our eligibility criteria. Using a meta-analysis based on a random effect model, we evaluated the cytotoxicity of cisplatin (IC50) after 48- or 72-h cell exposure. We found large differences between studies using a particular cell line. According to the random effect model, the 95% confidence intervals for IC50 were extremely wide. The heterogeneity of cisplatin IC50, as measured by the I2 index for all cancer cell lines, was over 99.7% at culture times of 48 or 72 h. Therefore, the variability between studies is due to experimental heterogeneity rather than chance. Despite the higher IC50 values after 48 h than after 72 h, the heterogeneity between the two culture periods did not differ significantly. This indicates that the duration of cultivation is not the main cause of heterogeneity. Therefore, the available data is diverse and not useful as a reference. We discuss possible reasons for the IC50 heterogeneity and advise researchers to conduct preliminary testing before starting experiments and not to solely rely on the published data. We hope that this systematic meta-analysis will provide valuable information for researchers searching for new cancer drugs using cisplatin as a reference drug.

Toxicological Impacts of TiO2 Nanoparticles on Growth, Photosynthesis Pigments, and Protein and Lipid Content of the Marine Microalga Tetraselmis Suecica.

Regarding the widespread use of titanium dioxide nanoparticles (TiO2NPs) in industry, many concerns have been raised about the risks of their potential release into aquatic ecosystems. Among the marine primary producers, Tetraselmis suecica is an ecologically important microalgae species used also as live feed in the shrimp culture industry. In the present study, the impacts of TiO2NPs on growth performance, photosynthetic pigments, lipid and protein content and its interaction with the cells of T. suecica were assessed. Based on the preliminary tests and OECD suggestion, concentrations of 5, 10, 50, 100, 200 and 400 mg/L TiO2NPs were applied to algal cells for 10 days. With increasing concentration, a decrease in T. suecica cell density was observed each day. TiO2NPs induced a half-maximal inhibitory concentration (IC50) of 106.26 mg/L on algal cells on the 3rd day. Chlorophyll a and b contents of the microalga decreased up to 56.08% and 52.74%, respectively following the exposure to TiO2NPs at 400 mg/L. TiO2NPs also decreased the algal contents of protein and lipid up to 7.21% and 50.64%, respectively at the highest concentration. Based on FTIR, FESEM with EDS and mapping analyses, the interaction of TiO2NPs with the T. suecica cells was revealed. The stocks of T. suecica could be damaged by the toxic effects of the released TiO2NPs affecting their application as live feed in mariculture.

Designing drug occupancy studies with PET neuroimaging: Sample size, occupancy ranges and analytical methods.

Molecular neuroimaging is today considered essential for evaluation of novel CNS drugs; it is used to quantify blood-brain barrier permeability, verify interaction with key target and determine the drug dose resulting in 50% occupancy, IC50. In spite of this, there has been limited data available to inform on how to optimize study designs. Through simulations, we here evaluate how IC50 estimation is affected by the (i) range of drug doses administered, (ii) number of subjects included, and (iii) level of noise in the plasma drug concentration measurements. Receptor occupancy is determined from PET distribution volumes using two different methods: the Lassen plot and Likelihood estimation of occupancy (LEO). We also introduce and evaluate a new likelihood-based estimator for direct estimation of IC50 from PET distribution volumes. For estimation of IC50, we find very limited added benefit in scanning individuals who are given drug doses corresponding to less than 40% receptor occupancy. In the range of typical PET sample sizes (5-20 subjects) each extra individual clearly reduces the error of the IC50 estimate. In all simulations, likelihood-based methods gave more precise IC50 estimates than the Lassen plot; four times the number of subjects were required for the Lassen plot to reach the same IC50 precision as LEO.

Cisplatin resistance-related multi-omics differences and the establishment of machine learning models.

OBJECTIVES: Platinum-based chemotherapies are currently the first-line treatment of non-small cell lung cancer. This study will improve our understanding of the causes of resistance to cisplatin, especially in lung adenocarcinoma (LUAD) and provide a reference for therapeutic decisions in clinical practice. METHODS: Cancer Cell Line Encyclopedia (CCLE), The Cancer Genome Atlas (TCGA) and Zhongshan hospital affiliated to Fudan University (zs-cohort) were used to identify the multi-omics differences related to platinum chemotherapy. Cisplatin-resistant mRNA and miRNA models were constructed by Logistic regression, classification and regression tree and C4.5 decision tree classification algorithm with previous feature selection performed via least absolute shrinkage and selection operator (LASSO). qRT-PCR and western-blotting of A549 and H358 cells, as well as single-cell Seq data of tumor samples were applied to verify the tendency of certain genes. RESULTS: 661 cell lines were divided into three groups according to the IC50 value of cisplatin, and the top 1/3 (220) with a small IC50 value were defined as the sensitive group while the last 1/3 (220) were enrolled in the insensitive group. TP53 was the most common mutation in the insensitive group, in contrast to TTN in the sensitive group. 1348 mRNA, 80 miRNA, and 15 metabolites were differentially expressed between 2 groups (P < 0.05). According to the LASSO penalized logistic modeling, 6 of the 1348 mRNAs, FOXA2, BATF3, SIX1, HOXA1, ZBTB38, IRF5, were selected as the associated features with cisplatin resistance and for the contribution of predictive mRNA model (all of adjusted P-values < 0.001). Three of 6 (BATF3, IRF5, ZBTB38) genes were finally verified in cell level and patients in zs-cohort. CONCLUSIONS: Somatic mutations, mRNA expressions, miRNA expressions, metabolites and methylation were related to the resistance of cisplatin. The models we created could help in the prediction of the reaction and prognosis of patients given platinum-based chemotherapies.

A review of alpha-glucosidase inhibitors from plants as potential candidates for the treatment of type-2 diabetes.

Diabetes mellitus is a multifactorial global health disorder that is rising at an alarming rate. Cardiovascular diseases, kidney damage and neuropathy are the main cause of high mortality rates among individuals with diabetes. One effective therapeutic approach for controlling hyperglycemia associated with type-2 diabetes is to target alpha-amylase and alpha-glucosidase, enzymes that catalyzes starch hydrolysis in the intestine. At present, approved inhibitors for these enzymes are restricted to acarbose, miglitol and voglibose. Although these inhibitors retard glucose absorption, undesirable gastrointestinal side effects impede their application. Therefore, research efforts continue to seek novel inhibitors with improved efficacy and minimal side effects. Natural products of plant origin have been a valuable source of therapeutic agents with lesser toxicity and side effects. The anti-diabetic potential through alpha-glucosidase inhibition of plant-derived molecules are summarized in this review. Eight molecules (Taxumariene F, Akebonoic acid, Morusin, Rhaponticin, Procyanidin A2, Alaternin, Mulberrofuran K and Psoralidin) were selected as promising drug candidates and their pharmacokinetic properties and toxicity were discussed where available. Supplementary Information: The online version contains supplementary material available at 10.1007/s11101-021-09773-1.

Advancements in the development of multi-target directed ligands for the treatment of Alzheimer's disease.

Alzheimer's disease (AD) is a multifactorial irreversible neurological disorder which results in cognitive impairment, loss of cholinergic neurons in synapses of the basal forebrain and neuronal death. Exact pathology of the disease is not yet known however, many hypotheses have been proposed for its treatment. The available treatments including monotherapies and combination therapies are not able to combat the disease effectively because of its complex pathological mechanism. A multipotent drug for AD has the potential to bind or inhibit multiple targets responsible for the progression of the disease like aggregated Aß, hyperphosphorylated tau proteins, cholinergic and adrenergic receptors, MAO enzymes, overactivated N-methyl-d-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor etc. The traditional approach of one disease-one target-one drug has been rationalized to one drug-multi targets for the chronic diseases like AD and cancer. Thus, over the last decade research focus has been shifted towards the development of multi target directed ligands (MTDLs) which can simultaneously inhibit multiple targets and stop or slow the progression of the disease. The MTDLs can be more effective against AD and eliminate any possibility of drug-drug interactions. Many important active pharmacophore units have been fused, merged or incorporated into different scaffolds to synthesize new potent drugs. In the current article, we have described various hypothesis for AD and effectiveness of the MTDLs treatment strategy is discussed in detail. Different chemical scaffolds and their synthetic strategies have been described and important functionalities are identified in the chemical scaffold that have the potential to bind to the multiple targets. The important leads identified in this study with MTDL characteristics have the potential to be developed as drug candidates for the effective treatment of AD.

Do differences in cell lines and methods used for calculation of IC50 values influence categorisation of drugs as P-glycoprotein substrates and inhibitors?

In vitro bidirectional assays are employed to determine whether a drug is a substrate and/or inhibitor of P-glycoprotein (P-gp) transport. Differences between cell lines and calculation methods can lead to variations in the determination of efflux ratios (ER) and IC50 values used to classify a drug as a P-gp substrate and inhibitor, respectively.Information was collected from the literature on ER and IC50 values with digoxin as the probe substrate using different cell lines and inhibition calculation methods. Predictive performance was evaluated by comparing [Igut]/IC50 ratios versus reported in vivo results.For known P-gp substrates, 50% of the drugs had their highest ER value in MDCK-MDR1 cells while 81% had their lowest ER value in Caco-2 cells. For 30 drugs with inhibition data, lower mean IC50 values were often observed with the Caco-2 cells and calculations based on ER. Based on the cut-off criteria of [Igut]/IC50 ≥ 10, there were no significant differences in positive or negative predictive values based on either cell line or calculation method for the drugs.Within this limited dataset, differences between cell lines or IC50 calculation methods do not seem to impact the prediction of in vivo P-gp inhibitor classification.

Enzyme kinetic approach for mechanistic insight and predictions of in vivo starch digestibility and the glycaemic index of foods.

BACKGROUND: Starch is a principal dietary source of digestible carbohydrate and energy. Glycaemic and insulinaemic responses to foods containing starch vary considerably and glucose responses to starchy foods are often described by the glycaemic index (GI) and/or glycaemic load (GL). Low GI/GL foods are beneficial in the management of cardiometabolic disorders (e.g., type 2 diabetes, cardiovascular disease). Differences in rates and extents of digestion of starch-containing foods will affect postprandial glycaemia. SCOPE AND APPROACH: Amylolysis kinetics are influenced by structural properties of the food matrix and of starch itself. Native (raw) semi-crystalline starch is digested slowly but hydrothermal processing (cooking) gelatinises the starch and greatly increases its digestibility. In plants, starch granules are contained within cells and intact cell walls can limit accessibility of water and digestive enzymes hindering gelatinisation and digestibility. In vitro studies of starch digestion by α-amylase model early stages in digestion and can suggest likely rates of digestion in vivo and expected glycaemic responses. Reports that metabolic responses to dietary starch are influenced by α-amylase gene copy number, heightens interest in amylolysis. KEY FINDINGS AND CONCLUSIONS: This review shows how enzyme kinetic strategies can provide explanations for differences in digestion rate of different starchy foods. Michaelis-Menten and Log of Slope analyses provide kinetic parameters (e.g., K m and k cat /K m ) for evaluating catalytic efficiency and ease of digestibility of starch by α-amylase. Suitable kinetic methods maximise the information that can be obtained from in vitro work for predictions of starch digestion and glycaemic responses in vivo.