THPdb2: compilation of FDA approved therapeutic peptides and proteins.

During the past 20 years, there has been a significant increase in the number of protein-based drugs approved by the US Food and Drug Administration (FDA). This paper presents THPdb2, an updated version of the THPdb database, which holds information about all types of protein-based drugs, including peptides, antibodies, and biosimilar proteins. THPdb2 contains a total of 6,385 entries, providing comprehensive information about 894 FDA-approved therapeutic proteins, including 354 monoclonal antibodies and 85 peptides or polypeptides. Each entry includes the name of therapeutic molecule, the amino acid sequence, physical and chemical properties, and route of drug administration. The therapeutic molecules that are included in the database target a wide range of biological molecules, such as receptors, factors, and proteins, and have been approved for the treatment of various diseases, including cancers, infectious diseases, and immune disorders.

Hypertensive Disorders of Pregnancy and Risk of Early Brain Abnormalities on MRI at Term among Infants Born at ≤32 Weeks' Gestational Age.

OBJECTIVE: To evaluate the proximal effects of hypertensive disorders of pregnancy (HDP) on a validated measure of brain abnormalities in infants born at ≤32 weeks' gestational age (GA) using magnetic resonance imaging (MRI) at term-equivalent age. STUDY DESIGN: In a multisite prospective cohort study, 395 infants born at ≤32 weeks' GA, underwent 3T MRI scan between 39 through 44 weeks' postmenstrual age. A single neuroradiologist, blinded to clinical history, evaluated the standardized Kidokoro global brain abnormality score as the primary outcome. We classified infants as HDP-exposed by maternal diagnosis of chronic hypertension, gestational hypertension, preeclampsia, or eclampsia. Linear regression analysis identified the independent effects of HDP on infant brain abnormalities, adjusting for histologic chorioamnionitis, maternal smoking, antenatal steroids, magnesium sulfate, and infant sex. Mediation analyses quantified the indirect effect of HDP mediated via impaired intrauterine growth and prematurity and remaining direct effects on brain abnormalities. RESULTS: 170/395 infants (43%) were HDP-exposed. Adjusted multivariable analyses revealed HDP-exposed infants had 27% (95% CI 5-53%) higher brain abnormality scores than those without HDP exposure (p=0.02), primarily driven by increased white matter injury/abnormality scores (p=0.01). Mediation analyses showed HDP-induced impaired intrauterine growth significantly (p=0.02) contributed to brain abnormality scores (22% of the total effect). CONCLUSIONS: Maternal hypertension independently increased the risk for early brain injury and/or maturational delays in infants born at ≤32 weeks' GA with an indirect effect of 22% resulting from impaired intrauterine growth. Enhanced prevention/treatment of maternal hypertension may mitigate the risk of infant brain abnormalities and potential neurodevelopmental impairments.

Does the evidence support in utero influences on later health and disease? A systematic review of highly cited Barker studies on developmental origins.

The developmental origins of health and disease hypothesis proposes that early exposure to adverse conditions during fetal development and early life have strong detrimental consequences on long-term health and susceptibility to chronic diseases. We conducted a systematic review to critically appraise Barker's highest cited publications using the risk-of-bias assessment tool (ROBINS-I) and investigate effects of overadjustment by later body weight. Our findings revealed that all included studies displayed high risks of bias, with particular concerns regarding confounding (8/8), selection of reported results (8/8), classification of exposure (7/8), selection of participants (5/8) and high rates of missing data (ranged from 15 to 87%). Later body weight was over-adjusted in most (6/8) of the studies. As all studies displayed high bias risk due to confounding, missing data and overadjustment, evidence is insufficient to support causal relationships between low birthweight and adult disease, warranting caution in clinical application. PROTOCOL REGISTRATION: PROSPERO CRD42023433179.

Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022.

Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was, in fact, widely predicted by seasonal forecasts. Here we argue that, in this case, a strong summer La Niña provided a window of opportunity to issue a much more confident forecast for extreme rainfall than average skill estimates would suggest. We explore ways of building forecast confidence via a physical understanding of dynamical mechanisms, perturbation experiments to isolate extreme drivers, and simple empirical relationships. We highlight the need for more detailed routine monitoring of forecasts, with improved tools, to identify regional climate extremes and hence utilise windows of opportunity to issue trustworthy and actionable early warnings.

TNFepitope: A webserver for the prediction of TNF-α inducing epitopes.

Tumor Necrosis Factor alpha (TNF-α) is a pleiotropic pro-inflammatory cytokine that is crucial in controlling the signaling pathways within the immune cells. Recent studies reported that higher expression levels of TNF-α are associated with the progression of several diseases, including cancers, cytokine release syndrome in COVID-19, and autoimmune disorders. Thus, it is the need of the hour to develop immunotherapies or subunit vaccines to manage TNF-α progression in various disease conditions. In the pilot study, we proposed a host-specific in-silico tool for predicting, designing, and scanning TNF-α inducing epitopes. The prediction models were trained and validated on the experimentally validated TNF-α inducing/non-inducing epitopes from human and mouse hosts. Firstly, we developed alignment-free (machine learning based models using composition-based features of peptides) methods for predicting TNF-α inducing peptides and achieved maximum AUROC of 0.79 and 0.74 for human and mouse hosts, respectively. Secondly, an alignment-based (using BLAST) method has been used for predicting TNF-α inducing epitopes. Finally, a hybrid method (combination of alignment-free and alignment-based method) has been developed for predicting epitopes. Hybrid approach achieved maximum AUROC of 0.83 and 0.77 on an independent dataset for human and mouse hosts, respectively. We have also identified potential TNF-α inducing peptides in different proteins of HIV-1, HIV-2, SARS-CoV-2, and human insulin. The best models developed in this study has been incorporated in the webserver TNFepitope (https://webs.iiitd.edu.in/raghava/tnfepitope/), standalone package and GitLab (https://gitlab.com/raghavalab/tnfepitope).

In Silico Tool for Identification, Designing, and Searching of IL13-Inducing Peptides in Antigens.

Interleukins are a distinctive class of molecules exhibiting various immune signaling functions. Immunoregulatory cytokine, Interleukin 13 (IL13), is primarily synthesized by activated T-helper 2 cells, mast cells, and basophils. IL13, is known to stimulate many allergic and autoimmune diseases, such as asthma, rheumatoid arthritis, systemic sclerosis, ulcerative colitis, airway hyperresponsiveness, glycoprotein hypersecretion, and goblet cell hyperplasia. In addition to such disorders, IL13 also leads to carcinogenesis by inhibiting tumor immunosurveillance. Due to its role in various diseases, predicting IL13-inducing peptides or regions in a protein is vital to designing safe protein vaccines and therapeutics. IL13pred is an in silico tool which aids in identifying, predicting, and designing IL13-inducing peptides. The IL13pred web server and standalone package is easily accessible at ( https://webs.iiitd.edu.in/raghava/il13pred/ ).

Early surgery in very preterm infants is associated with brain abnormalities on term MRI: a propensity score analysis.

OBJECTIVE: To investigate the association between exposure to surgery under general anesthesia and brain abnormalities and neurodevelopmental outcomes in very preterm infants. STUDY DESIGN: This prospective observational study includes 392 infants born at or below 32 weeks' gestational age. Participants completed brain MRI at term-equivalent age and Bayley-III assessment at 2 years corrected age. We evaluated the independent effects of surgery on brain MRI abnormalities and neurodevelopmental outcomes after propensity score matching. RESULTS: All infants completed brain MRI, and 341 (87%) completed neurodevelopmental testing. Forty-five received surgery. Surgery was associated with worse MRI abnormalities (p < 0.0001) but with none of the developmental outcomes after propensity score matching. The global brain abnormality score was associated with the Bayley Cognitive (p = 0.005) and Motor (p = 0.028) composite scores. CONCLUSIONS: Very preterm infants exposed to surgery under general anesthesia were at higher risk of brain abnormalities on MRI at term.

Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models.

In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.

Collection, compilation and analysis of bacterial vaccines.

BACKGROUND: Bacterial diseases are one of the leading causes of millions of fatalities worldwide, mainly due to antimicrobial resistance. The discovery of chicken cholera vaccine in 1879 revolutionized our fight against bacterial infections. Bacterial vaccines are proven to be highly effective in preventing many infectious diseases. Currently, various licensed vaccines are available against bacterial infections such as typhoid, diphtheria, cholera and tetanus in the market. In this study, we have attempted to compile different information regarding bacterial vaccines, their types, efficacy, mechanism of action, status, route of administration and other relevant details as a knowledgebase known as BacVacDB. METHODS: BacVacDB was implemented using Linux-Apache-MySQL-PHP. HTML, PHP, CSS and Javascript have been used to develop the front end and MySQL for the back end. The data was curated from several sources, including literature, databases and relevant web resources. RESULTS: This paper reviewed 371 vaccines against 30 human bacterial diseases maintained in BacVacDB, of which 167 are approved and 204 in clinical trials. This database provides the users an effortless search facility in the four modules, 'Search,' 'Browse,' 'External Links' and 'General Information'. In this systematic attempt, we also included the history of vaccines, their mechanism, types, route of administration and approving agencies. CONCLUSIONS: This knowledgebase has an intuitive interface that allows users to explore, search, and download information as well as to submit new bacterial vaccines (https://webs.iiitd.edu.in/raghava/bacvacdb/).

ToxinPred2: an improved method for predicting toxicity of proteins.

Proteins/peptides have shown to be promising therapeutic agents for a variety of diseases. However, toxicity is one of the obstacles in protein/peptide-based therapy. The current study describes a web-based tool, ToxinPred2, developed for predicting the toxicity of proteins. This is an update of ToxinPred developed mainly for predicting toxicity of peptides and small proteins. The method has been trained, tested and evaluated on three datasets curated from the recent release of the SwissProt. To provide unbiased evaluation, we performed internal validation on 80% of the data and external validation on the remaining 20% of data. We have implemented the following techniques for predicting protein toxicity; (i) Basic Local Alignment Search Tool-based similarity, (ii) Motif-EmeRging and with Classes-Identification-based motif search and (iii) Prediction models. Similarity and motif-based techniques achieved a high probability of correct prediction with poor sensitivity/coverage, whereas models based on machine-learning techniques achieved balance sensitivity and specificity with reasonably high accuracy. Finally, we developed a hybrid method that combined all three approaches and achieved a maximum area under receiver operating characteristic curve around 0.99 with Matthews correlation coefficient 0.91 on the validation dataset. In addition, we developed models on alternate and realistic datasets. The best machine learning models have been implemented in the web server named 'ToxinPred2', which is available at https://webs.iiitd.edu.in/raghava/toxinpred2/ and a standalone version at https://github.com/raghavagps/toxinpred2. This is a general method developed for predicting the toxicity of proteins regardless of their source of origin.

In silico tools and databases for designing cancer immunotherapy.

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

IL13Pred: A method for predicting immunoregulatory cytokine IL-13 inducing peptides.

BACKGROUND: Interleukin 13 (IL-13) is an immunoregulatory cytokine, primarily released by activated T-helper 2 cells. IL-13 induces the pathogenesis of many allergic diseases, such as airway hyperresponsiveness, glycoprotein hypersecretion, and goblet cell hyperplasia. In addition, IL-13 inhibits tumor immunosurveillance, leading to carcinogenesis. Since elevated IL-13 serum levels are severe in COVID-19 patients, predicting IL-13 inducing peptides or regions in a protein is vital to designing safe protein therapeutics particularly immunotherapeutic. OBJECTIVE: The present study describes a method to develop, predict, design, and scan IL-13 inducing peptides. METHODS: The dataset experimentally validated 313 IL-13 inducing peptides, and 2908 non-inducing homo-sapiens peptides extracted from the immune epitope database (IEDB). A total of 95 key features using the linear support vector classifier with the L1 penalty (SVC-L1) technique was extracted from the originally generated 9165 features using Pfeature. These key features were ranked based on their prediction ability, and the top 10 features were used to build machine learning prediction models. Various machine learning techniques were deployed to develop models for predicting IL-13 inducing peptides. These models were trained, tested, and evaluated using five-fold cross-validation techniques; the best model was evaluated on an independent dataset. RESULTS: Our best model based on XGBoost achieves a maximum AUC of 0.83 and 0.80 on the training and independent dataset, respectively. Our analysis indicates that certain SARS-COV2 variants are more prone to induce IL-13 in COVID-19 patients. CONCLUSION: The best performing model was incorporated in web-server and standalone package named 'IL-13Pred' for precise prediction of IL-13 inducing peptides. For large dataset analysis standalone package of IL-13Pred is available at (https://webs.iiitd.edu.in/raghava/il13pred/) webserver and over GitHub link: https://github.com/raghavagps/il13pred.

SARS-CoV-2 Infection in Patients with Down Syndrome, Congenital Heart Disease, and Pulmonary Hypertension: Is Down Syndrome a Risk Factor?

With increasing information available about the epidemiology, pathophysiology, and management of patients affected with severe acute respiratory syndrome corona virus-2 infection, patients with Down syndrome, congenital heart disease, airway obstruction, and pulmonary hypertension present a unique challenge. This case series describes 3 patients with Down syndrome and respiratory failure secondary to coronavirus infection.

Parental Perception of the Factors that Affect Diabetes Management in Youth.

IN BRIEF Diabetes management is challenging for youth. We developed a theoretical framework for the facilitators and barriers to diabetes management in youth from the perspective of parents.

A Prospective Analysis of Drug Interactions in Patients of Intensive Cardiac Care Unit.

INTRODUCTION: Drug-Drug Interaction (DDI) is a serious concern in cardiac patients due to polypharmacy. AIM: The present study was aimed to identify the potential DDI among hospitalized cardiac patients and evaluate the mechanism and severity of such interactions. MATERIALS AND METHODS: A prospective observational study was conducted in intensive cardiac care unit of a tertiary care hospital for six months. Patients aged 18 years and above and taking two or more drugs were included in the study. Medscape drug interaction checker was used to identify and analyze the pattern of potential DDI. RESULTS: Out of 500 patients, most of the patients were male (78.4%) in the age group of 50-60 years (31%). The most common diagnosis was acute coronary syndrome (57.2%). Out of total 2849 DDI, 2194 (77.01%) were pharmacodynamic, 586 (20.57%) were pharmacokinetic in nature while 69 (2.42%) drug pairs interacted by unknown mechanism. Majority of drug interactions were significant {2031 (71.29%)} in nature followed by minor {725(25.45%)} while serious drug interactions were observed in only 93 (3.26%) drug pairs. A positive correlation was observed between patient's age and number of drugs prescribed (r=0.178, p<0.001), number of drugs prescribed and potential Drug-Drug Interaction (pDDI) (r= 0.788, p<0.001) and between patient's age and pDDI (r=0.338, p<0.001). CONCLUSION: The risk of pDDI was more commonly observed in elderly male patients particularly with antiplatelet drugs like low dose aspirin and clopidogrel.

A systematic review of prescription pattern monitoring studies and their effectiveness in promoting rational use of medicines.

Prescription pattern monitoring studies (PPMS) are a tool for assessing the prescribing, dispensing and distribution of medicines. The main aim of PPMS is to facilitate rational use of medicines (RUM). There is paucity of published data analysing the effectiveness of PPMS. The present review has been done to assess the effectiveness of prescription pattern monitoring studies in promoting RUM. Data search was conducted on internet. A multitude of PPMS done on different classes of drugs were collected and analyzed. PPMS using WHO prescribing indicators were also included. The present article reviews various prescription pattern monitoring studies of drugs conducted all over country and abroad. It was observed in the majority of such studies that physicians do not adhere to the guidelines made by regulatory agencies leading to irrational use of medicines. This in turn leads to increased incidence of treatment failure, antimicrobial resistance and economic burden on the patient and the community as a whole. The treatment of diseases by the use of essential drugs, prescribed by their generic names, has been emphasized by the WHO and the National Health Policy of India. We conclude that the prescription monitoring studies provide a bridge between areas like rational use of drugs, pharmacovigilance, evidence based medicine, pharmacoeconomics, pharmacogenetics and ecopharmacovigilance. In India, this is the need of the hour to utilise the data generated by so many prescription pattern monitoring studies done in every state and on every drug, so that the main aim of promoting rational use of drugs is fulfilled.

Comparative study of high dose mono-therapy of amlodipine or telmisartan, and their low dose combination in mild to moderate hypertension.

INTRODUCTION: Hypertension is one of the major public health challenges worldwide. Angiotensin receptor blockers (ARBs) and Calcium channel blockers (CCBs) are among the first line antihypertensive drugs. However, optimal treatment strategies in mild to moderate hypertensives who failed to achieve blood pressure (BP) control with low-dose mono-therapy are not well established. This study was done to compare efficacy and safety of high dose mono-therapy of Amlodipine, Telmisartan and their low dose combination in mild to moderate hypertensives who failed to achieve BP control with low dose mono-therapy of either drug. MATERIALS AND METHODS: A total of 96 patients, fulfilling inclusion and exclusion criteria were enrolled in the study after obtaining informed consent. Patients were randomized into three treatment groups i.e. Telmisartan 80 mg, Amlodipine 10 mg and low dose combination of Telmisartan 40 mg +Amlodipine 5 mg once daily for two months. The systolic BP, Diastolic BP, and ADRs were recorded at 0, 2, 4, 8 weeks. RESULTS: In the present study, significant reduction of mean systolic blood pressure (SBP) and mean diastolic blood pressure (DBP) was seen in all the three treatment groups. Low dose combination of Amlodipine 5 mg and Telmisartan 40 mg showed statistically significant reduction in SBP as compared to Telmisartan 80 mg mono-therapy and in DBP as compared to Amlodipine 10 mg mono-therapy. Maximum adverse drug reactions (ADRs) were reported in Amlodipine mono-therapy group, like ankle oedema, constipation, headache and fatigue. DISCUSSION AND CONCLUSION: In term of BP control, low-dose combination therapy appears a better therapeutic approach than high-dose mono-therapy.

Antibiotic prescribing in various clinical departments in a tertiary care teaching hospital in northern India.

BACKGROUND: Antibiotic resistance is not only a problem for the individual patient; it also reduces the effectiveness of established treatment and has become a major threat to public health by increasing the complexity and cost of treatment and reducing the probability of a successful outcome. AIM: A prospective cross sectional study was carried out with the aim of identifying prescription pattern of antibiotics in a tertiary care teaching hospital in Northern India. MATERIALS AND METHODS: A total of 300 prescriptions were collected, collated and analysed from the indoor patients of MG hospital, Jaipur, India from the department of Medicine, Surgery and Orthopaedics. The prescribing and dispensing details of antibiotics from each prescription were recorded in the tabular form as mentioned in Data Acquisition form. Comparison of antibiotic prescribing practices among all the three departments was made by using Percentage method. RESULTS: Majority of prescriptions (51%) with single drug was prescribed in Medicine department, followed by 16% in surgery and only 2% in Orthopaedics. Prescriptions with 3 drugs were prescribed mostly in Orthopaedics (66%) followed by 46% in Surgery and 10% in Medicine. 51% prescriptions in Orthopaedics department were of Ceftriaxone+ Sulbactam+ Amikacin. Thirty four percent prescriptions in Medicine department were of Ceftriaxone. 18% prescriptions in Surgery department were of Ceftriaxone+ Sulbactam+ Tobramycin. CONCLUSION: This study clearly highlights the practice of Poly-Pharmacy and injudicious usage of antibiotics in hospital settings. The Government of India is planning to revise the antibiotic policy issued in 2011 and put a ban on over the counter availability of third generation antibiotics. General public awareness and sensitization of doctors and revision of clinical drug policy is the need of the hour to bring the changes at all possible level for the longterm and better clinical outcome in medical practice.