Differential expression of miRNAs and related mRNAs during heat stress in buffalo heifers.

The present experiment was aimed to study differential expression of miRNAs and related mRNAs during heat stress (HS) in buffalo heifers. Twelve Murrah buffalo heifers aged between 1.5 and 2.0 years, weighting between 250 and 300 Kg were randomly assigned into two equal groups. The animals were kept in the psychrometric chamber under Thermo-neutral (TN; THI = 72) and HS (THI = 87-90) conditions for 6 h every day between 1000 and 1600 h for 21 days. The blood sampling was done at 1500 h on 15th day of the experiment and physiological parameters viz. pulse rate (PR), respiratory rate (RR) and rectal temperature (RT) were recorded at 1500 h on day -5, -3, -1, 0, +1, +3, +5 with respect to blood sampling. PBMCs were used for extraction of miRNAs and total RNA; and first strand cDNA was synthesized. qPCR was performed for relative gene expression studies. Physiological, hematological (erythrocytic indices), biochemical (triglycerides, urea, ALT, AST, LDH), redox (SOD, ROS) and endocrine parameters (T4) altered significantly (P < 0.05) during HS as compared to TN. Out of eight targeted miRNAs only four were expressed in buffalo heifers. The relative expression of bta-mir-142, bta-mir-1248 and bta-mir-2332 was significantly (P < 0.05) up-regulated whereas expression of bta-mir-2478 was significantly (P < 0.05) down-regulated during HS as compared to TN. The relative expression of the predicted target genes i.e. HSF1, HSP60, HSP70, HSPA8 and HSP90 were significantly (P < 0.05) up-regulated whereas HSF4 expression was significantly (P < 0.05) down-regulated during HS as compared to TN. It can be concluded that a THI of 87-90 could lead to a moderate HS in buffalo heifers. Differential expression studies of miRNAs and related mRNAs in present study deciphers the role of miRNAs in the heat tolerance in buffalo heifers.

Appropriate THI model and its threshold for goats in semi-arid regions of India.

The present study was attempted to identify an appropriate THI model and threshold THI for goats of semi-arid regions of India. Sixty non-pregnant goats each from Jamunapari and Barbari breeds were selected for the study. The study was conducted from last week of February to first week of June, during which average THI ranged between 53 and 92. Pulse rate (PR), respiration rate (RR) and rectal temperature (RT) were recorded at 1430 h on alternate days from six goats of each breed randomly during the experiment. Nine THI models were used to calculate THI. An appropriate THI model was predicted on the basis of correlation between THIs calculated from each model and physiological responses. The data of physiological parameters were linked to the THI calculated from identified THI model and threshold THI for each parameter was determined using segmented regression analysis (SegReg Software). The THI models; THI1{(1.8 × Tdb+32)-[(0.55-0.0055 × RH) × (1.8 × Tdb-26.8)]} and THI8{(0.8 × Tdb)+[(RH/100) × (Tdb-14.4)]+46.4)} were found to be equally appropriate for assessing environmental heat stress. Threshold THIs with respect to PR, RR and RT in Jamunapari goat were 71.78, 75.14 and 85.94, respectively and in Barbari goats, threshold THIs for PR and RR were 79.48 and 84.40, respectively. A threshold THI could not be identified for RT in Barbari goats. It can be concluded that THI1 and THI8 were the appropriate THI models for measuring environmental heat stress in goats. Results suggested that PR is the first physiological parameter which alters after the onset of heat stress and is followed by changes in RR and RT. On the basis of differential threshold THIs, it can be concluded that Barbari is better adapted than Jamunapari goats in semi-arid regions of India.

Diverse models for anti-HIV activity of purine nucleoside analogs.

BACKGROUND: Purine nucleoside analogs (PNAs) constitute an important group of cytotoxic drugs for the treatment of neoplastic and autoimmune diseases. In the present study, classification models have been developed for the prediction of the anti-HIV activity of purine nucleoside analogs. RESULTS: The topochemical version of superaugmented pendentic index-4 has been proposed and successfully utilized for the development of models. A total of 60 2D and 3D molecular descriptors (MDs) of diverse nature were selected for building the classification models using decision tree (DT), random forest (RF), support vector machine (SVM), and moving average analysis (MAA). The values of most of these descriptors for each of the analogs in the dataset were computed using the Dragon software (version 5.3). An in-house computer program was also employed to calculate additional MDs which were not included in the Dragon software. DT, RF, and SVM correctly classified the analogs into actives and inactives with an accuracy of 89 %, 83 %, and 78 %, respectively. MAA-based models predicted the anti-HIV activity of purine nucleoside analogs with a non-error rate up to 98 %. Therapeutic active spans of the suggested MAA-based models not only showed more potency but also exhibited enhanced safety as revealed by comparatively high values of selectivity index (SI). The statistical importance of the developed models was appraised via intercorrelation analysis, specificity, sensitivity, non-error rate, and Matthews correlation coefficient. CONCLUSIONS: High predictability of the proposed models clearly indicates an immense potential for developing lead molecules for potent but safe anti-HIV purine nucleoside analogs.

Role of moving average analysis for development of multi-target (Q)SAR models.

In modern drug discovery era, multi target- quantitative structure activity relationship [mt- (Q)SAR] approaches have emerged as novel and powerful alternatives in the field of in-silico drug design so as to facilitate the discovery of new chemical entities with multiple biological activities. Amongst various machine learning approaches, moving average analysis (MAA) has frequently exhibited high accuracy of prediction of diverse biological activities against different biological targets and experimental conditions. Role of MAA in developing (Q)SAR models for prediction of single/dual or multi target activity has been briefly reviewed in the present article. Subsequently, MAA was successfully utilized for developing mt-(Q)SAR models for simultaneous prediction of anti-Plasmodium falciparum and anti-Trypanosoma brucei rhodesiense activities of benzyl phenyl ether derivatives. The statistical significance of models was assessed through intercorrelation analysis, sensitivity, specificity and Matthew's correlation coefficient. Proposed MAA based models were also validated using test set. High predictability of the order of 80% to 95% amalgamated with safety (indicated by high value of selectivity index) of proposed mt-(Q)SAR models justifies use of MAA in developing models in order to obtain more realistic and accurate results for prediction of anti-protozal activity against multiple targets. Active ranges of the proposed models can play a significant role in the development of novel, potent, versatile and safe anti-protozoal drugs with improved profile in terms of both anti-Plasmodium falciparum and anti-Trypanosoma brucei rhodesiense activities.

Detour matrix-based adjacent path eccentric distance sum indices for QSAR/QSPR. Part I: development and evaluation.

In the present study, three detour matrix-based topological indices (TIs) termed as adjacent path eccentric distance sum indices 1-3 (denoted by (A)ξ(1)(PDS), (A)ξ(2)(PDS) and (A)ξ(3)(PDS)) as well as their topochemical versions (denoted by (A)ξ(1c)(PDS), (A)ξ(2c)(PDS) and (A)ξ(3c)(PDS)) have been conceptualised. Values of the proposed TIs were computed for all possible cyclic and acyclic structures containing three, four, five vertices using an in-house computer programme. Proposed TIs were evaluated for discriminating power, degeneracy, intercorrelation and sensitivity towards branching as well relative position of substituent(s) in cyclic structures. Mathematical properties of one of the proposed TIs were also studied. Exceptionally high discriminating power, high sensitivity towards branching as well as relative position(s) of substituent(s) in cyclic structures and negligible degeneracy offer proposed indices a vast potential for use in characterisation of structures, similarity/dissimilarity studies, lead identification and optimisation, combinatorial library design and quantitative structure-activity/property/toxicity/pharmacokinetic relationship studies so as to facilitate drug design.

Detour matrix-based adjacent path eccentric distance sum indices for (Q)SAR/QSPR. Part II: application in development of models for COX-2 inhibitory activity of indomethacin derivatives.

In present study, adjacent path eccentric distance sum indices proposed in Part-I of the manuscript were successfully utilised for the development of models for cycloxygenase-2 (COX-2) inhibitory activity. Values of diverse molecular descriptors (MDs) for each of 38 indomethacin analogues involved in the dataset were computed. A total of 55 diverse MDs were ultimately shortlisted for further analysis. The suitable models were developed using decision tree (DT), random forest (RF) and moving average analysis (MAA). The DT identified the proposed topological index (TI)-(A)ξ(3)(PDS) as one of the important indices. The accuracy of prediction of DT, RF and MAA-based models varied from 81.58% to 97.37%. The statistical significance of proposed models was assessed through inter-correlation analysis, sensitivity, specificity, non-error rate and Mathews correlation coefficient. Proposed models offer vast potential for providing lead structures for the development of potent anti-inflammatory agents devoid of COX-1 side effects.

Models for H3 receptor antagonist activity of sulfonylurea derivatives.

The histamine H3 receptor has been perceived as an auspicious target for the treatment of various central and peripheral nervous system diseases. In present study, a wide variety of 60 2D and 3D molecular descriptors (MDs) were successfully utilized for the development of models for the prediction of antagonist activity of sulfonylurea derivatives for histamine H3 receptors. Models were developed through decision tree (DT), random forest (RF) and moving average analysis (MAA). Dragon software version 6.0.28 was employed for calculation of values of diverse MDs of each analogue involved in the data set. The DT classified and correctly predicted the input data with an impressive non-error rate of 94% in the training set and 82.5% during cross validation. RF correctly classified the analogues into active and inactive with a non-error rate of 79.3%. The MAA based models predicted the antagonist histamine H3 receptor activity with non-error rate up to 90%. Active ranges of the proposed MAA based models not only exhibited high potency but also showed improved safety as indicated by relatively high values of selectivity index. The statistical significance of the models was assessed through sensitivity, specificity, non-error rate, Matthew's correlation coefficient and intercorrelation analysis. Proposed models offer vast potential for providing lead structures for development of potent but safe H3 receptor antagonist sulfonylurea derivatives.

Fourth generation detour matrix-based topological descriptors for QSAR/QSPR - Part-2: application in development of models for prediction of biological activity.

Augmented path eccentric connectivity topochemical indices (reported in part-1 of the manuscript) along with 42 diverse non-correlating molecular descriptors (shortlisted from a large pool of 2D and 3D MDs) were successfully utilised for the development of models through decision tree, random forest and moving average analysis for the prediction of antitubercular activity of aza and diazabiphenyl analogues of active compound (6S)-2-Nitro-{[4-(trifluoromethoxy)benzyl]oxy}-6,7-dihydro-5H-imidazo[2,1-b][1,3] oxazine (PA-824). The statistical significance of the proposed models was assessed through overall accuracy of prediction, intercorrelation analysis, sensitivity, specificity and Matthew's correlation coefficient (MCC). The accuracy of prediction of the proposed models varied from a minimum of 81% to a maximum of ∼99%. High accuracy of prediction amalgamated with high MCC values clearly indicates robustness of the proposed models. The said models offer a vast potential for providing lead structures for the development of potent antitubercular drugs.

Models for the prediction of melanocortin-4 receptor agonist activity of 4-substituted piperidin-4-ol.

In the present study both classification and correlation techniques have been successfully employed for the development of the models of diverse nature for the prediction of melanocortin 4-receptor (MC4 R) agonist activity using a dataset comprising of 56 analogues of 4-substituted piperidine-4-ol derivatives. Decision tree (DT), random forest (RF), moving average analysis (MAA) and multiple linear regression (MLR) were utilised for development of the said models. The statistical significance of models was assessed through specificity, sensitivity, overall accuracy, Mathew's correlation coefficient (MCC) and intercorrelation analysis. High accuracy of prediction up to 98% was observed using these models. Proposed models offer vast potential for providing lead structures for the development of potent therapeutic agents for the treatment of male sexual dysfunction.

Classification models for safe drug molecules.

Frequent failure of drug candidates during development stages remains the major deterrent for an early introduction of new drug molecules. The drug toxicity is the major cause of expensive late-stage development failures. An early identification/optimization of the most favorable molecule will naturally save considerable cost, time, human efforts and minimize animal sacrifice. (Quantitative) Structure Activity Relationships [(Q)SARs] represent statistically derived predictive models correlating biological activity (including desirable therapeutic effect and undesirable side effects) of chemicals (drugs/toxicants/environmental pollutants) with molecular descriptors and/or properties. (Q)SAR models which categorize the available data into two or more groups/classes are known as classification models. Numerous techniques of diverse nature are being presently employed for development of classification models. Though there is an increasing use of classification models for prediction of either biological activity or toxicity, the future trend will naturally be towards the development of classification models capable of simultaneous prediction of biological activity, toxicity, and pharmacokinetic parameters so as to accelerate development of bioavailable safe drug molecules.

Fourth generation detour matrix-based topological indices for QSAR/QSPR - part-1: development and evaluation.

In the present study, four detour matrix-based Topological Indices (TIs) termed as augmented path eccentric connectivity indices 1-4 (denoted by (AP)ξ(1)(C), (AP)ξ(2)(C), (AP)ξ(3)(C) and (AP)ξ(4)(C)) as well as their topochemical versions (denoted by (AP)ξ(1c)(C), (AP)ξ(2c)(C), (AP)ξ(3c)(C) and (AP)ξ(4c)(C)) have been conceptualised. A modified detour matrix termed as chemical detour matrix (Δ(c)) has also been proposed so as to facilitate computation of index values of topochemical versions of the said TIs. Values of the proposed TIs were computed for all the possible structures containing three, four and five vertices using an in-house computer program. The said TIs exhibited exceptionally high discriminating power and high sensitivity towards branching/relative position of substituent(s) in cyclic structures amalgamated with negligible degeneracy. Due care was taken during the development of TIs so as to ensure that reduction in index values of complex chemical structures to be within reasonable limits without compromising discriminating power. The mathematical properties of one of the proposed TIs have also been studied. With exceptionally high discriminating power, high sensitivity towards branching as well as relative position(s) of substituents in cyclic structures and negligible degeneracy, the proposed indices offer a vast potential for use in characterisation of structures, similarity/dissimilarity studies, lead identification and optimisation, combinatorial library design and quantitative structure-activity/property/toxicity/pharmacokinetic relationship studies.

Prediction of pharmacokinetic parameters.

In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.

Predicting biological activity: computational approach using novel distance based molecular descriptors.

Four novel distance based molecular descriptors termed as superpendentic eccentric distance sum indices 1-4 (denoted by:∫P-1EDS, ∫P-2EDS, ∫P-3EDS and ∫P-4EDS) as well as their topochemical counterparts (denoted by:∫cP-1EDS, ∫cP-2EDS, ∫cP-3EDS and ∫cP-4EDS) have been conceptualized and developed in the present study. The sensitivity towards branching, discriminating power, and degeneracy of the proposed novel descriptors were investigated. Utility of these indices was investigated for development of models through decision tree and moving average analysis for the prediction of human corticotropin releasing factor-1 receptor binding affinity of substituted pyrazines. A wide variety of 46 2D and 3D molecular descriptors including proposed indices was employed for development of models through decision tree and moving average analysis. The calculation of most of these descriptors for each compound of the dataset was performed using online E-Dragon software (version 1.0). An in-house computer programme was also employed to calculate additional topological descriptors which did not figure in E-Dragon software. The decision tree classified and correctly predicted the input data with an impressive accuracy of 92% in the training set and 71% during cross-validation. A total of three descriptors, identified by decision tree, were subsequently utilized for development of suitable models using moving average analysis. These models predicted human corticotropin releasing factor-1 receptor binding affinity with an accuracy of ≥85%. The statistical significance of models was assessed through sensitivity, specificity and Matthew's correlation coefficient. High discriminating power, high sensitivity towards branching amalgamated with negligible degeneracy offer proposed descriptors a vast potential for use in the quantitative structure-activity/property/toxicity relationships so as to facilitate drug design.

Classification models for anticancer activity.

Despite significant research in understanding of neoplastic diseases, the success rate for oncology drugs is relatively very low. A major challenge before the scientific community is to design new chemical entities that will be highly selective for cancer cells so as to minimize side effects. Classification models (CMs) models play a prominent role in prediction of the biological properties of newly designed compounds before their synthesis and prevent non-optimal use of resources. Though correlation models far outnumber classification models for development of various therapeutic agents but the significance of classification models for development of anti-cancer agents can not be underestimated. Various techniques employed for development of classification models for anti-cancer activity have been briefly reviewed. Moreover, successful use of some of these classification techniques for the development of models for anti-proliferative activity has been illustrated using a data set comprising of 53 analogues of N-Benzoylated phenoxazines and phenothiazines. Resulting classification models with high degree of accuracy can play a vital role in providing lead structures for the development of novel anti-proliferative agents for cancer chemotherapy.

Models for anti-tumor activity of bisphosphonates using refined topochemical descriptors.

An in silico approach comprising of decision tree (DT), random forest (RF) and moving average analysis (MAA) was successfully employed for development of models for prediction of anti-tumor activity of bisphosphonates. A dataset consisting of 65 analogues of both nitrogen-containing and non-nitrogen-containing bisphosphonates was selected for the present study. Four refinements of eccentric distance sum topochemical index termed as augmented eccentric distance sum topochemical indices 1-4 [formula: see text] have been proposed so as to significantly augment discriminating power. Proposed topological indices (TIs) along with the exiting TIs (>1,400) were subsequently utilized for development of models for prediction of anti-tumor activity of bisphosphonates. A total of 43 descriptors of diverse nature, from a large pool of molecular descriptors, calculated through E-Dragon software (version 1.0) and an in-house computer program were selected for development of suitable models by employing DT, RF and MAA. DT identified two TIs as most important and classified the analogues of the dataset with an accuracy of 97% in training set and 90.7% in tenfold cross-validated set. Random forest correctly classified the analogues with an accuracy of 89.2%. Four independent models developed through MAA predicted the activity of analogues of the dataset with an accuracy of 87.6% to 89%. The statistical significance of proposed models was assessed through intercorrelation analysis, specificity, sensitivity and Matthew's correlation coefficient. The proposed models offer a vast potential for providing lead structures for development of potent anti-tumor agents for treatment of cancer that has spread to the bone.

Models for prediction of (V)600(E)BRAF and melanoma cells growth inhibitory activities of pyridoimidazolones.

Targeted inhibition of activated BRAF mutation has emerged as a most promising and putative therapeutic approach for the anticancer drug development. In the present study, an in-silico approach using decision tree and moving average analysis has been applied to a data set comprising of 43 analogues of pyridoimidazolones for development of models for prediction of both (V)600(E)BRAF and melanoma cells (BRAF WM266.4) growth inhibitory activities. A decision tree was mainly employed for determining the importance of molecular descriptors (n=46). The value of majority of these descriptors for each analogue in the dataset was computed using E-Dragon software (version 1.0). The decision tree learned the information from the input data with an accuracy of 98% and correctly predicted the cross-validated (10-fold) data with accuracy up to 79%. A total of three non-correlating descriptors, identified best by the decision tree analysis, were subsequently utilized for development of suitable models using moving average analysis. These proposed models resulted in the prediction of (V)600(E)BRAF inhibitory activity (IC50) and melanoma cells growth (SRB GI50) inhibitory activity with an overall accuracy of ≥90%. The statistical significance of models/descriptors was assessed through intercorrelation analysis, sensitivity, specificity and Matthew's correlation coefficient.

Models for cannabinoid-1 receptor antagonistic activity of substituted 2-(3-pyrazolyl)-1,3,4-oxadiazoles.

Antagonism of cannabinoid receptor-1 has emerged as a most promising therapeutic target for the development of anti-obesity drugs. In the present study, an in silico approach using decision tree, random forest and moving average analysis has been applied to a data set comprising of 76 analogues of substituted 2-(3-pyrazolyl)-1,3,4-oxadiazoles for development of models for prediction of antagonistic activity of cannabinoid receptor-1. A total of 46 2D and 3D molecular descriptors of diverse nature were employed for decision tree and random forest analysis. The values of majority of these descriptors for each analogue involved in the dataset were computed using E-Dragon software (version 1.0). Random forest correctly classified the analogues into active and inactive with an accuracy of 95%. A decision tree was also utilized for determining the importance of molecular descriptors. The decision tree learned the information from the input data with an accuracy of 99% and correctly predicted the cross-validated (10 fold) data with an accuracy up to 90%. Finally, three molecular descriptors of diverse nature (including best descriptor identified by decision tree analysis) were subsequently used to build suitable models using moving average analysis. These models resulted in the prediction of cannabinoid receptor-1 antagonistic activity with an accuracy of 95-96%. High predictability of proposed models offer vast potential for providing lead structures for the development of potent cannabinoid receptor-1 antagonists for the treatment of obesity.

Topological models for prediction of physico-chemical, pharmacokinetic and toxicological properties of antihistaminic drugs using decision tree and moving average analysis.

Various topostructural and topochemical indices were used to encode the structureal features of antihistaminic drugs. The values of 18 indices for each drug comprising the dataset were computed using an in-house computer program. In the present study, decision tree and moving average analysis were used to predict physico-chemical (log P), pharmacokinetic (T(max)) and toxicological properties (LD(50)) of antihistaminic drugs. A decision tree was constructed for each property to determine the importance of Topological Indices (TIs). Single topological index based models were developed using moving average analysis. The tree learned the information from the input data with an accuracy of >94% and predicted the cross-validated (10-fold) data with an accuracy of upto 71%. Moving average analysis resulted in single index based models with an accuracy upto 80%.

Urea co-inclusion compounds of 13 cis-retinoic acid for simultaneous improvement of dissolution profile, photostability and safe handling characteristics.

13-cis Retinoic acid (cis-RA), a synthetic retinoid used in the treatment of severe acne, is known to exhibit extremely low aqueous solubility and high photosensitivity. In this study, urea, a well-known adductor for linear compounds, was successfully employed for the adduction of cis-RA - a substituted cyclic organic compound. Formation of urea inclusion compounds was confirmed by FTIR, DSC and XRD. A modified Zimmerschied calorimetric method was employed for the estimation of the minimum amount of rapidly adductible endocyte (RAE) required for adduction of cis-RA in urea. Urea-cis-RA-RAE inclusion compounds containing varying proportions of guests were prepared and their thermal behaviour studied by DSC. The inclusion compounds were found to have an improved dissolution profile as demonstrated by an overall increase in the dissolution efficiency. An accelerated photostability study, conducted as per Q1B ICH guidelines, revealed that co-inclusion of cis-RA in urea delayed photo-degradation of the drug when compared with that of the pure drug. The results suggest the possibility of exploiting co-inclusion of the drug in a urea host lattice for improved solubility, stability and reduced handling problems for cis-RA.