A novel framework based on explainable AI and genetic algorithms for designing neurological medicines.

The advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its central component, has resulted in the mechanization of numerous previously labor-intensive activities. The use of in silico tools has become prevalent in the design of biopharmaceuticals. Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences.

Artificial intelligence-based model for predicting the minimum inhibitory concentration of antibacterial peptides against ESKAPEE pathogens.

In response to environmental threats, pathogens make several changes in their genome, leading to antimicrobial resistance (AMR). Due to AMR, the pathogens do not respond to antibiotics. Amongst drug-resistant pathogens, the ESKAPEE group of bacteria poses a major threat to humans, and therefore World Health Organization has given them the highest priority status. Antibacterial peptides (ABPs) are a family of peptides found in nature that play a crucial role in the innate immune systems of organisms. These ABPs offer several advantages over widely used antibiotics. As a result, they have recently received a lot of attention as potential replacements for currently available antibiotics. But it is expensive and time-consuming to identify ABPs from natural sources. Thus, wet lab researchers employ various tools to screen promising ABPs rapidly. However, the main limitation of the existing tools is that they do not provide the minimum inhibitory concentration values against the ESKAPEE pathogens for the identified ABP. To address this, in the current work, we developed ESKAPEE-MICpred, a two-input model that utilizes transfer learning and ensemble learning techniques. The concept of ensemble learning was realized by combining the decisions provided by deep learning algorithms, whereas the concept of transfer learning was realized by utilizing pretrained amino acid embeddings. The proposed model has been deployed as a web server at https://eskapee-micpred.anvil.app/ to aid the scientific community.

EnDL-HemoLyt: Ensemble Deep Learning-based Tool for Identifying Therapeutic Peptides with Low Hemolytic Activity.

Low hemolytic therapeutic peptides have gained an edge over small molecule-based medicines. However, finding low hemolytic peptides in laboratory is time-consuming, costly and necessitates the use of mammalian red blood cells. Therefore, wet-lab researchers often perform in-silico prediction to select low hemolytic peptides before proceeding with in-vitro testing. The in-silico tools available for this purpose have following limitations: (i) They do not provide predictions for peptides having N/C terminal modifications. (ii) Data is food for AI; however, datasets used to create existing tools do not contain peptide data generated over past eight years. (iii) Performance of available tools is also low. Therefore, a novel framework has been proposed in current work. Proposed framework utilizes recent dataset and uses ensemble learning technique to combine the decisions produced by bidirectional long short-term memory, bidirectional temporal convolutional network, and 1-dimensional convolutional neural network deep learning algorithms. Deep learning algorithms are capable of extracting features themselves from data. However, instead of relying solely on deep learning-based features (DLF), handcrafted features (HCF) were also provided so that deep learning algorithms can learn features that are missing from HCF, and a better feature vector can be constructed by concatenating HCF and DLF. Additionally, ablation studies were carried out to understand the roles of an ensemble algorithm, HCF, and DLF in the proposed framework. Ablation studies found that the ensemble algorithm, HCF and DLF are crucial components of proposed framework, and there is a decrease in performance on eliminating any of them. Mean value of performance metrics, namely Acc, Sn, Pr, Fs, Sp, Ba, and Mcc obtained by proposed framework for test data is ≈ 87, 85, 86, 86, 88, 87, and 73, respectively. To aid scientific community, model developed from proposed framework has been deployed as a web server at https://endl-hemolyt.anvil.app/.

Genome-wide association study reveals novel genomic regions governing agronomic and grain quality traits and superior allelic combinations for Basmati rice improvement.

Background: Basmati is a speciality segment in the rice genepool characterised by explicit grain quality. For the want of suitable populations, genome-wide association study (GWAS) in Basmati rice has not been attempted. Materials: To address this gap, we have performed a GWAS on a panel of 172 elite Basmati multiparent population comprising of potential restorers and maintainers. Phenotypic data was generated for various agronomic and grain quality traits across seven different environments during two consecutive crop seasons. Based on the observed phenotypic variation, three agronomic traits namely, days to fifty per cent flowering, plant height and panicle length, and three grain quality traits namely, kernel length before cooking, length breadth ratio and kernel length after cooking were subjected to GWAS. Genotyped with 80K SNP array, the population was subjected to principal component analysis to stratify the underlying substructure and subjected to the association analysis using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model. Results: We identified 32 unique MTAs including 11 robust MTAs for the agronomic traits and 25 unique MTAs including two robust MTAs for the grain quality traits. Six out of 13 robust MTAs were novel. By genome annotation, six candidate genes associated with the robust MTAs were identified. Further analysis of the allelic combinations of the robust MTAs enabled the identification of superior allelic combinations in the population. This information was utilized in selecting 77 elite Basmati rice genotypes from the panel. Conclusion: This is the first ever GWAS study in Basmati rice which could generate valuable information usable for further breeding through marker assisted selection, including enhancing of heterosis.

Role of Volumetric Modulated Arc Technique Radiotherapy in a Rare Case of Intraductal Apocrine Carcinoma of Parotid Gland.

Introduction Apocrine carcinoma, a cancer of sweat glands, is very rare, with a global incidence of 0.0049 to 0.0173 cases per 100,000 persons annually. It is usually found in axilla and anogenital areas. The intraductal apocrine variety of salivary duct carcinoma in the parotid gland is very rare and aggressive and may be due to ectopic sweat glands in the parotid gland duct or metaplastic change of the salivary duct epithelium. It usually presents in an advanced stage. Even though surgery is the standard of care in most head and neck cancers, there are no standard guidelines for the treatment of intraductal apocrine parotid carcinoma, which is different from other head and neck cancers due to its rare incidence, aggressive behavior, and poor prognosis. Case presentation We present a rare case of intraductal apocrine salivary duct carcinoma of the left parotid gland, presented in a locally advanced stage with very high chances of recurrence after surgery, and discuss the role of volumetric modulated arc technique radiotherapy in its management. Conclusion Intraductal apocrine salivary duct carcinoma usually has androgen receptor expression, and lack of expression is associated poor prognosis. Even with complete resection, it has a high recurrence rate. Volumetric modulated arc technique radiotherapy (VMAT) decreases recurrence and increases survival by irradiating the areas more likely of recurrence, with minimal toxicity to surrounding normal tissues.

The effect of time to surgery in neck of femur fracture patients with ASA Grade of 3 and above.

INTRODUCTION: Hip fractures are an important cause of morbidity and mortality. Early surgery has been shown to reduce mortality rates and surgical complications. The American Society of Anesthesiologists (ASA) grade is a widely used tool to assess preoperative health of patients. This study aims to assess is whether delay in surgical time has a greater impact on the mortality rates for high risk patients. METHOD: Retrospective study using the National Hip Fracture Database (NHFD) of 4883 neck of femur fracture patients. Time of surgery, ASA grade, reason for delay and mortality at 120 days was analysed, using statistical analysis software. RESULTS: There was a significant increase in mortality (p < 0.001) with increasing ASA grade. Surgical delays of more than 36 hours increased mortality by 2.9%. The impact of delaying surgery became more pronounced as the ASA grade increased. ASA 3 and above had an optimum time to surgery of between 12 and 24 hours giving the statistically significant lowest mortality rate (p = 0.004). DISCUSSION: Surgical delay beyond the 36-hour target for surgery has a greater impact on mortality for patients with higher ASA grades. The effect is most profound in the high-risk ASA grade 5 patients with delayed patients showing a 37.5% increase in mortality in this group. This would imply that by prioritising this higher risk group and operating on it within a specific time frame there would be a subsequent fall in mortality associated with neck of femur fractures.

Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM.

Fungal infections or mycosis cause a wide range of diseases in humans and animals. The incidences of community acquired; nosocomial fungal infections have increased dramatically after the emergence of COVID-19 pandemic. The increase in number of patients with immunodeficiency / immunosuppression related diseases, resistance to existing antifungal compounds and availability of limited therapeutic options has triggered the search for alternative antifungal molecules. In this direction, antifungal peptides (AFPs) have received a lot of interest as an alternative to currently available antifungal drugs. Although the AFPs are produced by diverse population of living organisms, identifying effective AFPs from natural sources is time-consuming and expensive. Therefore, there is a need to develop a robust in silico model capable of identifying novel AFPs in protein sequences. In this paper, we propose Deep-AFPpred, a deep learning classifier that can identify AFPs in protein sequences. We developed Deep-AFPpred using the concept of transfer learning with 1DCNN-BiLSTM deep learning algorithm. The findings reveal that Deep-AFPpred beats other state-of-the-art AFP classifiers by a wide margin and achieved approximately 96% and 94% precision on validation and test data, respectively. Based on the proposed approach, an online prediction server is created and made publicly available at https://afppred.anvil.app/. Using this server, one can identify novel AFPs in protein sequences and the results are provided as a report that includes predicted peptides, their physicochemical properties and motifs. By utilizing this model, we identified AFPs in different proteins, which can be chemically synthesized in lab and experimentally validated for their antifungal activity.

Deep-AVPpred: Artificial Intelligence Driven Discovery of Peptide Drugs for Viral Infections.

Rapid increase in viral outbreaks has resulted in the spread of viral diseases in diverse species and across geographical boundaries. The zoonotic viral diseases have greatly affected the well-being of humans, and the COVID-19 pandemic is a burning example. The existing antivirals have low efficacy, severe side effects, high toxicity, and limited market availability. As a result, natural substances have been tested for antiviral activity. The host defense molecules like antiviral peptides (AVPs) are present in plants and animals and protect them from invading viruses. However, obtaining AVPs from natural sources for preparing synthetic peptide drugs is expensive and time-consuming. As a result, an in-silico model is required for identifying new AVPs. We proposed Deep-AVPpred, a deep learning classifier for discovering AVPs in protein sequences, which utilises the concept of transfer learning with a deep learning algorithm. The proposed classifier outperformed state-of-the-art classifiers and achieved approximately 94% and 93% precision on validation and test sets, respectively. The high precision indicates that Deep-AVPpred can be used to propose new AVPs for synthesis and experimentation. By utilising Deep-AVPpred, we identified novel AVPs in human interferons- α family proteins. These AVPs can be chemically synthesised and experimentally verified for their antiviral activity against different viruses. The Deep-AVPpred is deployed as a web server and is made freely available at https://deep-avppred.anvil.app, which can be utilised to predict novel AVPs for developing antiviral compounds for use in human and veterinary medicine.

AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom.

With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genome databases. Antimicrobial host defense proteins provide protection against invading microbes. But confirming the antimicrobial function of host proteins by wet-lab experiments is expensive and time consuming. Therefore, there is a need to develop an in silico tool to identify the antimicrobial function of proteins. In the current study, we developed a model AniAMPpred by considering all the available antimicrobial peptides (AMPs) of length $\in $[10 200] from the animal kingdom. The model utilizes a support vector machine algorithm with deep learning-based features and identifies probable antimicrobial proteins (PAPs) in the genome of animals. The results show that our proposed model outperforms other state-of-the-art classifiers, has very high confidence in its predictions, is not biased and can classify both AMPs and non-AMPs for a diverse peptide length with high accuracy. By utilizing AniAMPpred, we identified 436 PAPs in the genome of Helobdella robusta. To further confirm the functional activity of PAPs, we performed BLAST analysis against known AMPs. On detailed analysis of five selected PAPs, we could observe their similarity with antimicrobial proteins of several animal species. Thus, our proposed model can help the researchers identify PAPs in the genome of animals and provide insight into the functional identity of different proteins. An online prediction server is also developed based on the proposed approach, which is freely accessible at https://aniamppred.anvil.app/.

Thumb CMCJ prosthetic total joint replacement: a systematic review.

Thumb carpometacarpal joint (CMCJ) arthritis is a common and painful condition. Thumb CMCJ prosthetic replacement aims to restore thumb biomechanics and improve pain and function. Early reviews demonstrated a lack of high-quality studies, but more recently a significant number of higher-quality studies have been published. This review provides a concise and systematic overview of the evidence to date.A systematic review of several databases was conducted according to PRISMA guidelines. Studies evaluating the outcomes of thumb CMCJ prosthetic total joint replacement were included. Data extracted included patient-reported outcome measures (PROMs), pain scores, range of motion, strength, survival rates and complications.A total of 56 studies met all inclusion criteria and were analysed. There was one randomized controlled trial, three prospective comparative cohort studies, five retrospective comparative cohort studies, and 47 descriptive cohort studies. The reported studies included 2731 patients with 3048 thumb total CMCJ prosthetic joint replacements. Follow up ranged from 12 months to 13.1 years.In general, good results were demonstrated, with improvements in PROMs, pain scores and strength. Failure rates ranged from 2.6% to 19.9% depending upon implant studied. Comparative studies demonstrated promising results for replacement when compared to resection arthroplasty, with modest improvements in PROMs but at a cost of increased rates of complications.Studies reporting outcomes in thumb CMCJ prosthetic total joint replacement are increasing in both number and quality. Failure, in terms of loosening and dislocation, remains a concern, although in the medium-term follow up for modern implants this issue appears to be lower when compared to their predecessors.Functional outcomes also look promising compared to resection arthroplasty, but further high-quality studies utilizing a standardized resection arthroplasty technique and modern implants, together with standardized core outcome sets, will be of value. Cite this article: EFORT Open Rev 2021;6:316-330. DOI: 10.1302/2058-5241.6.200152.

Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec.

The overuse of antibiotics has led to emergence of antimicrobial resistance, and as a result, antibacterial peptides (ABPs) are receiving significant attention as an alternative. Identification of effective ABPs in lab from natural sources is a cost-intensive and time-consuming process. Therefore, there is a need for the development of in silico models, which can identify novel ABPs in protein sequences for chemical synthesis and testing. In this study, we propose a deep learning classifier named Deep-ABPpred that can identify ABPs in protein sequences. We developed Deep-ABPpred using bidirectional long short-term memory algorithm with amino acid level features from word2vec. The results show that Deep-ABPpred outperforms other state-of-the-art ABP classifiers on both test and independent datasets. Our proposed model achieved the precision of approximately 97 and 94% on test dataset and independent dataset, respectively. The high precision suggests applicability of Deep-ABPpred in proposing novel ABPs for synthesis and experimentation. By utilizing Deep-ABPpred, we identified ABPs in the tail protein sequences of Streptococcus bacteriophages, chemically synthesized identified peptides in lab and tested their activity in vitro. These ABPs showed potent antibacterial activity against selected Gram-positive and Gram-negative bacteria, which confirms the capability of Deep-ABPpred in identifying novel ABPs in protein sequences. Based on the proposed approach, an online prediction server is also developed, which is freely accessible at https://abppred.anvil.app/. This web server takes the protein sequence as input and provides ABPs with high probability (>0.95) as output.

Early Results of the ARPE Arthroplasty Versus Trapeziectomy for the Treatment of Thumb Carpometacarpal Joint Osteoarthritis.

BACKGROUND: Thumb carpometacarpal joint arthroplasty for osteoarthritis may hold advantages over trapeziectomy by preserving range of motion, whilst providing stability and preventing thumb shortening. METHODS: We compare functional and satisfaction outcomes scores, radiological shortening and complication rates between patients treated with trapeziectomy and those receiving the ARPE thumb CMCJ arthroplasty. RESULTS: Seventy-five trapeziectomies and one hundred and ten ARPE arthroplasties were performed over the study period. Both treatments resulted in significant improvements in functional scores. When matching patients according to pre-operative function, patients receiving the ARPE arthroplasty had better post-operative function (Quick DASH: trapeziectomy = 25.1, ARPE = 16.8). More patients receiving the ARPE arthroplasty were satisfied with their treatment (trapeziectomy = 7.8/10, ARPE = 8.7/10) and would have the same treatment again (trapeziectomy = 76%, ARPE = 89%). The ARPE also resulted in less thumb shortening. However the ARPE arthroplasty is associated with a higher complication rate, with 14% of patients requiring further surgery at a mean of 2 years follow up (95% implant survival). CONCLUSIONS: Both trapeziectomy and the ARPE CMCJ arthroplasty are effective treatment options for thumb CMCJ osteoarthritis. Arthroplasty may offer potential advantages in terms of post-operative function and patient satisfaction. However the risk of complications and requirement for further surgery is greater and must be carefully considered during patient selection and pre-operative counselling.

Complications related to blood donation: A multicenter study of the prevalence and influencing factors in voluntary blood donation camps in Karnataka, India.

INTRODUCTION: Complications associated with blood donation significantly lower odds of subsequent donations. The aim of the study is to assess the prevalence of complications related to blood donation, identify the influencing factors, and come up with suggestions for minimizing discomfort to donors and making outdoor voluntary blood donation camps safer. MATERIALS AND METHODS: This study covered 181 blood donation camps organized by Sankalp India Foundation where 16 blood banks participated from 01-04-2011 to 01-08-2014 in Karnataka. Uniform protocols for donor selection, predonation preparation, counseling, postdonation care, and refreshments were used. The postdonation complications were recorded on a form immediately, after they were observed. RESULTS: We observed 995 (3.2%) complications in 30,928 whole blood donations. Of these 884 (2.86%) mild, 77 (0.25%) moderate, and 5 (0.02%) severe complications were observed. Local symptoms (blood outside vessels, pain, and allergy) contributed 1.0%, and generalized symptoms (vasovagal reaction) contributed 2.2% to all the complications. CONCLUSION: We observed 322 complications for every 10,000 donations. Since 27 out of every 10000 experience moderate and severe complication, the readiness to manage complications is crucial. Women donors, young donors, and donors with a lower weight are at a significantly greater risk of experiencing complications, highlighting the need for specific guidelines for the management of higher risk donor groups. Complications varied significantly between various blood banks. Predonation hydration was effective in limiting complications with generalized symptoms. We recommend a robust donor hemovigilance program for voluntary blood donation for monitoring complications and enable assessment of effectiveness and implementation of appropriate interventions.

Feature Surfaces in Symmetric Tensor Fields Based on Eigenvalue Manifold.

Three-dimensional symmetric tensor fields have a wide range of applications in solid and fluid mechanics. Recent advances in the (topological) analysis of 3D symmetric tensor fields focus on degenerate tensors which form curves. In this paper, we introduce a number of feature surfaces, such as neutral surfaces and traceless surfaces, into tensor field analysis, based on the notion of eigenvalue manifold. Neutral surfaces are the boundary between linear tensors and planar tensors, and the traceless surfaces are the boundary between tensors of positive traces and those of negative traces. Degenerate curves, neutral surfaces, and traceless surfaces together form a partition of the eigenvalue manifold, which provides a more complete tensor field analysis than degenerate curves alone. We also extract and visualize the isosurfaces of tensor modes, tensor isotropy, and tensor magnitude, which we have found useful for domain applications in fluid and solid mechanics. Extracting neutral and traceless surfaces using the Marching Tetrahedra method can cause the loss of geometric and topological details, which can lead to false physical interpretation. To robustly extract neutral surfaces and traceless surfaces, we develop a polynomial description of them which enables us to borrow techniques from algebraic surface extraction, a topic well-researched by the computer-aided design (CAD) community as well as the algebraic geometry community. In addition, we adapt the surface extraction technique, called A-patches, to improve the speed of finding degenerate curves. Finally, we apply our analysis to data from solid and fluid mechanics as well as scalar field analysis.

HPLC-DAD Method for the Pharmacokinetic Interaction Study of Atorvastatin with Pioglitazone and Cholestyramine in Wistar Rats.

Carotid intima-media thickness is used as a surrogate marker for cardiovascular complications in diabetes mellitus. The combination of atorvastatin and pioglitazone was found to be effective in reducing the thickness of the carotid intima-media layer. The method of RP-HPLC coupled with a diode array detector (DAD) was developed for the pharmacokinetic interaction study of atorvastatin with pioglitazone and cholestyramine, respectively, in Wistar rats. Atorvastatin (ATR) and pioglitazone (PIO) were resolved on a C18 column with a mobile phase composed of 48% methanol, 19% acetonitrile, and 33% 10 mM ammonium formate (v/v/v; pH 3.5±0.3, by formic acid) and a 260 nm detection wavelength on the diode array detector. The method was validated according to international standards with good reproducibility and linear response; mean (r) 0.9987 and 0.9972 to ATR and PIO, respectively. The coefficients of variation of intra- and interassay precision ranged between 4.95-8.12 and 7.29-9.67, respectively. Pharmacokinetic parameters were determined in rats following an oral administration of atorvastatin in the presence and absence of pioglitazone and also with cholestyramine. Compared with the control given atorvastatin alone, the Cmax and AUC of atorvastatin were merely unchanged in rats with the co-administration of pioglitazone, while they decreased by nearly 21 and 15%, respectively, with the concurrent use of cholestyramine. There were no significant changes in Tmax and the plasma half-life (T1/2 ) of atorvastatin in both cases. The performed experiment demonstrated that the presented method was suitable for the estimation and pharmacokinetic interaction study of atorvastatin with pioglitazone and cholestyramine in Wistar rat plasma.

Simple RP-HPLC method for determination of triple drug combination of valsartan, amlodipine and hydrochlorothiazide in human plasma.

A simple RP-HPLC method for the quantification of valsartan (VAL), amlodipine (AML) and hydrochlorothiazide (HCT) in human plasma was developed and validated. VAL, AML and HCT were resolved using a Gemini C18 column and mobile phase gradient starting from 20 % acetonitrile and 80 % 10 mmol L(-1) ammonium formate (V/V, pH 3.5 ± 0.2, by formic acid) to 70 % acetonitrile and 30 % 10 mmol L(-1) ammonium formate, over 20 minutes, with a flow rate of 1 mL min(-1). The samples were purified by protein precipitation and extraction. Telmisartan was used as internal standard. The method was validated according to USFDA and EMEA guidelines with good reproducibility and linear responses R = 0.9985 (VAL), 0.9964 (AML), and 0.9971 (HCT). RSDs of intra- and inter-day precision ranged 2.2-8.1 and 4.6-11.7 %, respectively, for all three drugs. Mean extraction recoveries of three QCs for the triple drug combination were 76.5 (VAL), 72.0 (AML) and 73.0 (HCT) % for human plasma. Although the LC-MS/MS method is more sensitive than HPLC, HPLC is still suitable for preliminary pharmacokinetic study. The experiments performed demostrated that simultaneous determination of all components of the triple drug combination in human plasma can be done by this method. Proposed method can be also used for guidance to the LC-MS/MS method.

RP-HPLC-DAD method for determination of olmesartan medoxomil in bulk and tablets exposed to forced conditions.

A simple, sensitive and precise RP-HPLC-DAD method was developed and validated for the determination of olmesartan medoxomil (AT-II receptor blocker) in the presence of its degradation products. Olmesartan medoxomil and all the degradation products were resolved on a C(18) column with the mobile phase composed of methanol, acetonitrile and water (60:15:25, V/V/V, pH 3.5 by orthophosphoric acid) at 260 nm using a photodiode array detector. The method was linear over the concentration range of 1-18 microg mL(-1) and precise with RSD < 1 % in intra- and inter-day study. Excellent recoveries of 99.3 +/- 0.9 to 100.8 +/- 1.2% proved the accuracy of the method. Developed method was specific, as indicated by chromatographic resolution > 2.0 for each peak and sensitive with LOD 0.03 microg mL(-1) and LOQ 0.1 microg mL(-1). The method was used to study the drug degradation behavior under forced conditions. Four degradation products (DP-I, II, III, IV) were formed during the degradation study in 0.1 mol L(-1) HCl whereas only DP-I, II and III were formed in water, 0.01 mol L(-1) NaOH and 3% H(2)O(2). No significant thermal or photolytic degradation was observed in solid drug. The method was applied successfully for the assay of olmesartan medoxomil in the tablet dosage form.

Pair wise binding affinity: 3D QSAR studies on a set of triazolo [1, 5-a] quinoxalines as antagonists of AMPA and KA receptors.

The glutamate receptor system is implicated in the development and maintenance of epileptic seizures and it has been reported that compounds showing high affinity for both AMPA and KA binding sites are more potent anticonvulsants than compounds having selective affinity toward AMPA or KA receptor. These outcomes make such inhibitors future potential antiepileptic drugs. So, the pair wise binding affinity for AMPA and KA receptors inhibition was proposed by using the addition between biological activities of ligands. This approach for evaluation of pair wise binding affinity was exemplified using set of triazolo [1,5-a] quinoxaline for AMPA and KA receptors. The biological activity towards AMPA and KA receptors (expressed as -log IC(5O)) was taken as a dependent variable for building CoMFA and CoMSIA models. The resulting models show the ways of increasing binding affinity to both AMPA and KA receptors as potential target for epilepsy. The statistically significant results show that pair wise CoMFA and CoMSIA models are better then individual models. The resulting cross-validated r(2)(CV) value 0.806 for CoMFA is greater then 0.780 for CoMSIA pair wise model. The non-cross validated run giving a coefficient of determination r(2) value of 0.946 and 0.908 for CoMFA and CoMSIA respectively, provided a good correlation between the observed and computed affinities of the compounds.

Synthesis of 4-benzyl-1,3-thiazole derivatives as potential anti-inflammatory agents: an analogue-based drug design approach.

A series of novel 4-Benzyl-1,3-thiazole derivatives was synthesized by applying analogue-based drug design approach and they were screened for anti-inflammatory activity. Darbufelone (CI 1004) a dual COX/LOX inhibitor, served as a lead molecule for designing a molecular scaffold. The derivatives with the 1,3 thiazole molecular scaffold bearing a side chain at position-2 resembling that of Romazarit (Ro-31-3948) were synthesized. The substitution at the second position of thiazole scaffold consisted of either carbalkoxy amino or aryl amino side chain. The introduction of an NH linker at the second position was the bioisoteric approach to impart the metabolic stability to the carbalkoxy side chains in designed molecules so as to avoid the likelihood of generating toxic moieties, like in Romazarit, which was withdrawn due to its toxicity profile. An important outcome of this study is the optimization of the substitution at the second position of the thiazole scaffold in eliciting better biological activity. The biological activity exhibited by the two designed series were in the order of carbalkoxy amino series > phenyl amino series. Molecule RS31 had emerged to be best compound in the whole series, having the side chain -NH-(C=O)O-R which resemble to Romazerit with 1,3 thiazole scaffold and substituted phenyl carbonyl group at fifth position derived from the retro-analysis of Darbufelone. This novel three-point pharmacophore, which is necessarily evolved from a lead-based drug design strategy, has opened up new avenues in designing of molecules acting on more than one rate-limiting step along the inflammatory cascade.

3D-QSAR studies for the binding affinity toward (R, S)-2-amino-3-(3-hydroxy-5-methylisoxazol-4-yl)-propionic acid receptor.

An approach for binding affinity evaluation is suggested and exemplified using a set of triazolo [1,5-a] quinoxaline for the (R, S)-2-amino-3-(3-hydroxy-5-methylisoxazol-4-yl)-propionic acid (AMPA) receptor. Biological activity toward the AMPA receptor (expressed as -log IC50) was taken as a dependent variable for building Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) models. The resulting models show the ways of increasing the binding affinity to the AMPA receptor as a potential target for epilepsy. The statistically significant results show that the cross-validated r2CV value (0.766) for the CoMFA model is greater than (0.758) for the CoMSIA model. The non-cross validated run giving the coefficient of determination r2 values of 0.944 and 0.919 for CoMFA and CoMSIA, respectively, provided good correlation between the observed and computed affinities of the training set compounds. The resulting CoMFA and CoMSIA models indicate that steric, electrostatic, hydrophobic (lipophilic), hydrogen bond donor and acceptor substituents play a significant role in increasing the binding affinity and selectivity of the compounds toward the AMPA receptor.