Once weekly selinexor, carfilzomib and dexamethasone in carfilzomib non-refractory multiple myeloma patients.

BACKGROUND: Proteasome inhibitors (PIs), including carfilzomib, potentiate the activity of selinexor, a novel, first-in-class, oral selective inhibitor of nuclear export (SINE) compound, in preclinical models of multiple myeloma (MM). METHODS: The safety, efficacy, maximum-tolerated dose (MTD) and recommended phase 2 dose (RP2D) of selinexor (80 or 100 mg) + carfilzomib (56 or 70 mg/m2) + dexamethasone (40 mg) (XKd) once weekly (QW) was evaluated in patients with relapsed refractory MM (RRMM) not refractory to carfilzomib. RESULTS: Thirty-two patients, median prior therapies 4 (range, 1-8), were enrolled. MM was triple-class refractory in 38% of patients and 53% of patients had high-risk cytogenetics del(17p), t(4;14), t(14;16) and/or gain 1q. Common treatment-related adverse events (all/Grade 3) were thrombocytopenia 72%/47% (G3 and G4), nausea 72%/6%, anaemia 53%/19% and fatigue 53%/9%, all expected and manageable with supportive care and dose modifications. MTD and RP2D were identified as selinexor 80 mg, carfilzomib 56 mg/m2, and dexamethasone 40 mg, all QW. The overall response rate was 78% including 14 (44%) ≥ very good partial responses. Median progression-free survival was 15 months. CONCLUSIONS: Weekly XKd is highly effective and well-tolerated. These data support further investigation of XKd in patients with MM.

The COVID-19 Ontology.

MOTIVATION: The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development. RESULTS: The ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute. AVAILABILITYAND IMPLEMENTATION: COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SEAweb: the small RNA Expression Atlas web application.

We present the Small RNA Expression Atlas (SEAweb), a web application that allows for the interactive querying, visualization and analysis of known and novel small RNAs across 10 organisms. It contains sRNA and pathogen expression information for over 4200 published samples with standardized search terms and ontologies. In addition, SEAweb allows for the interactive visualization and re-analysis of 879 differential expression and 514 classification comparisons. SEAweb's user model enables sRNA researchers to compare and re-analyze user-specific and published datasets, highlighting common and distinct sRNA expression patterns. We provide evidence for SEAweb's fidelity by (i) generating a set of 591 tissue specific miRNAs across 29 tissues, (ii) finding known and novel bacterial and viral infections across diseases and (iii) determining a Parkinson's disease-specific blood biomarker signature using novel data. We believe that SEAweb's simple semantic search interface, the flexible interactive reports and the user model with rich analysis capabilities will enable researchers to better understand the potential function and diagnostic value of sRNAs or pathogens across tissues, diseases and organisms.

Systems Toxicology Approach for Testing Chemical Cardiotoxicity in Larval Zebrafish.

Transcriptomic approaches can give insight into molecular mechanisms underlying chemical toxicity and are increasingly being used as part of toxicological assessments. To aid the interpretation of transcriptomic data, we have developed a systems toxicology method that relies on a computable biological network model. We created the first network model describing cardiotoxicity in zebrafish larvae-a valuable emerging model species in testing cardiotoxicity associated with drugs and chemicals. The network is based on scientific literature and represents hierarchical molecular pathways that lead from receptor activation to cardiac pathologies. To test the ability of our approach to detect cardiotoxic outcomes from transcriptomic data, we have selected three publicly available data sets that reported chemically induced heart pathologies in zebrafish larvae for five different chemicals. Network-based analysis detected cardiac perturbations for four out of five chemicals tested, for two of them using transcriptomic data collected up to 3 days before the onset of a visible phenotype. Additionally, we identified distinct molecular pathways that were activated by the different chemicals. The results demonstrate that the proposed integrational method can be used for evaluating the effects of chemicals on the zebrafish cardiac function and, together with observed cardiac apical end points, can provide a comprehensive method for connecting molecular events to organ toxicity. The computable network model is freely available and may be used to generate mechanistic hypotheses and quantifiable perturbation values from any zebrafish transcriptomic data.

Comparative Pharmacokinetic Profiles of a Novel Low-dose Micronized-isotretinoin 32 mg Formulation and Lidose-isotretinoin 40 mg in Fed and Fasted Conditions: Two Open-label, Randomized, Crossover Studies in Healthy Adult Participants.

Two open-label, crossover studies compared the bioavailability of Micronized-isotretinoin 32 mg and Lidose-isotretinoin 40 mg in healthy adults. In the fed bioequivalence/food-effect study, participants (n = 71) received single doses of fed-state Micronized-isotretinoin 32 mg, fed-state Lidose-isotretinoin 40 mg and fasted-state Micronized-isotretinoin 32 mg. In the fasting study, participants (n = 18) received single doses of fasted-state Micronized-isotretinoin 32 mg and fasted-state Lidose-isotretinoin 40 mg. Bioavailability was assessed by isotretinoin LnAUC0­t, LnAUC0­∞ and LnCmax in blood samples taken pre-dosing and over 96 h post-dosing. The 90% confidence intervals for baseline-adjusted least squares geometric mean ratios for LnAUC0­t, LnAUC0­∞ and LnCmax fell within the 80­125% range for bioequivalence for fed-state Micronized-isotretinoin 32 mg vs. fed-state Lidose-isotretinoin 40 mg. Fasted-state Micronized-isotretinoin 32 mg had ~2 times higher bioavailability than fasted-state Lidose-isotretinoin 40 mg. Food had no effect on the rate and a marginal effect on the extent of absorption of Micronized-isotretinoin 32 mg.

Design, preparation, and evaluation of liposomal gel formulations for treatment of acne: in vitro and in vivo studies.

The study highlights the significance of co-application of bioactive components into liposomal gel formulations and their comparison to azithromycin for treatment of Acne. A Design of Experiments (DoE) approach was utilized to obtain optimized liposomal formulation encapsulating curcumin, with size and zeta potential of ∼100 nm and ∼14 mV, respectively, characterized by DLS, HR-TEM, FESEM, and AFM. The curcumin liposomal dispersion depicted excellent stability over the period of 60 days, which was further converted in gel form using Carbopol. Pharmacokinetics of curcumin-loaded liposomal gel showed that Tmax for curcumin was achieved within 1 h of post application in both stratum corneum and skin, indicating quick penetration of nano-sized liposomes. Stratum corneum depicted Cmax of 688.3 ng/mL and AUC0-t of 5857.5 h × ng/mL, while the skin samples displayed Cmax of 203.3 ng/gm and AUC0-t of 2938.1 h × ng/gm. Lauric acid and azithromycin liposomal gel formulations were prepared as per the optimum parameters obtained by DoE. In antibacterial activity using agar diffusion assay, lauric acid gel formulation revealed ∼1.5 fold improved antibacterial effect than curcumin gel formulation. Interestingly, their co-application (1:1) exhibited significantly enhanced antibacterial effect against both macrolide-sensitive (1.81 versus 1.25 folds) and resistant strains of P. acnes (2.93 versus 1.22 folds) than their individual counterparts. The in vivo studies in rat ear model displayed a ∼2 fold reduction in comedones count and cytokines (TNF-α and IL-1ß) on co-application with curcumin and lauric acid liposomal gel compared to placebo treated group.

Safety, tolerability, and preliminary activity of IMGN529, a CD37-targeted antibody-drug conjugate, in patients with relapsed or refractory B-cell non-Hodgkin lymphoma: a dose-escalation, phase I study.

Background CD37 is expressed on B-cell lymphoid malignancies, thus making it an attractive candidate for targeted therapy in non-Hodgkin lymphoma (NHL). IMGN529 is an antibody-drug conjugate comprising a CD37-binding antibody linked to the maytansinoid DM1, a potent anti-mitotic agent. Methods This first-in-human, phase 1 trial recruited adult patients with relapsed or refractory B-cell NHL. The primary objective was to determine the maximum tolerated dose (MTD) and recommended phase 2 dose. Secondary objectives were to evaluate safety, pharmacokinetics, and preliminary clinical activity. IMGN529 was administered intravenously once every 3 weeks, and dosed using a conventional 3 + 3 dose-escalation design. Results Forty-nine patients were treated at doses escalating from 0.1 to 1.8 mg/kg. Dose limiting toxicities occurred in eight patients and included peripheral neuropathy, febrile neutropenia, neutropenia, and thrombocytopenia. The most frequent treatment-emergent adverse events were fatigue (39%), neutropenia, pyrexia, and thrombocytopenia (each 37%). Adverse events led to treatment discontinuation in 10 patients (20%). Eight patients (16%) had treatment-related serious adverse events, the most common being grade 3 febrile neutropenia. The MTD (with growth factor support) was 1.4 mg/kg every 3 weeks. IMGN529 plasma exposure increased monotonically with dose and was consistent with target-mediated drug disposition. Five (13%) of 39 response-evaluable patients achieved an objective response (one complete response and four partial responses), four of which occurred in the subgroup of patients with diffuse large B-cell lymphoma. Conclusions The manageable safety profile of IMGN529 and preliminary evidence of activity, particularly in DLBCL patients, support the continued development of this novel CD37-targeting agent.

High-dose melphalan and peripheral blood stem cell transplantation for light-chain amyloidosis with cardiac involvement.

High-dose melphalan (HDM) plus stem cell transplantation is an effective treatment for light-chain amyloidosis (AL), but is associated with high treatment-related mortality in patients with cardiac involvement. We studied 187 patients with cardiac involvement with AL who underwent HDM between 1996 and 2008. The median age was 57 years and the median time from diagnosis to HDM was 3.6 months. Half of the patients received reduced-dose melphalan (100-160 mg/m(2)). The median overall survival (OS) was 66 months, 54 months from diagnosis and HDM, respectively, and 91 patients (49%) were alive at the last follow-up 52 months (median) from HDM. Thirty patients (16%) died within 100 days of transplantation; only low serum albumin predicted early deaths. Overall, hematologic response (HR) and cardiac responses were seen in 66% and 41% of patients, respectively. The median OS for patients with and without HR was not reached and 22 months, respectively (P < .01); and for those with any decrease and no decrease in N-terminal-pro-brain natriuretic peptide was not reached and 26 months, respectively (P < .01). In multivariate analysis of baseline factors, only reduced-dose melphalan predicted shorter OS. HDM is feasible in patients with cardiac amyloidosis, and achievement of HR and organ response is associated with improved survival.

Efficacy and safety of once weekly selinexor 40 mg versus 60 mg with pomalidomide and dexamethasone in relapsed and/or refractory multiple myeloma.

Objective: To identify the optimal dose of selinexor in combination with pomalidomide and dexamethasone (SPd). Methods: An analysis of efficacy and safety of 2 once-weekly selinexor regimens (60 mg and 40 mg) with pomalidomide and dexamethasone (SPd-60 and SPd-40, respectively) given to patients with relapsed/refractory multiple myeloma (RRMM) in the STOMP (NCT02343042) and XPORT-MM-028 (NCT04414475) trials. Results: Twenty-eight patients (60.7% males, median age 67.5 years) and 20 patients (35.0% males, median age 65.5 years) were analyzed in the SPd-40 and SPd-60 cohorts, respectively. Overall response rate was 50% (95% confidence interval [CI] 30.6-69.4%) and 65% (95% CI 40.8-84.6%), respectively. Very good partial response or better was reported in 28.6% (95% CI 13.2-48.7%) and 30.0% (95% CI 11.9-54.3%) of patients, respectively. Among 27 responders in both cohorts, the 12-month sustained response rate was 83.3% (95% CI 64.7-100.0%) for SPd-40 and 28.1% (95% CI 8.9-88.8%) for SPd-60. Median progression-free survival was 18.4 months (95% CI 6.5 months, not evaluable [NE]) and 9.5 months (95% CI 7.6 months-NE) for SPd-40 and SPd-60, respectively. Twenty-four-month survival rates were 64.2% (95% CI 47.7-86.3%) for SPd-40 and 51.1% (95% CI 29.9-87.5%) for SPd-60. Treatment-emergent adverse events (TEAEs) included neutropenia (all grades: SPd-40 64.3% versus SPd-60 75.0%), anemia (46.4% versus 65.0%), thrombocytopenia (42.9% versus 45.0%), fatigue (46.4% versus 75.0%), nausea (32.1% versus 70.0%) and diarrhea (28.6% versus 35.0%). Conclusion: The all-oral combination of SPd exhibited preliminary signs of efficacy and was generally tolerable in patients with RRMM. The overall risk-benefit profile favored the SPd-40 regimen.

Efficacy of retreatment with immunomodulatory drugs (IMiDs) in patients receiving IMiDs for initial therapy of newly diagnosed multiple myeloma.

The efficacy of retreatment with immunomodulatory drugs (IMiDs) among patients with multiple myeloma who received this class of drugs for initial therapy is unknown. We studied 140 patients who received either thalidomide-dexamethasone (81; 58%) or lenalidomide-dexamethasone (59; 42%) as first-line therapy of multiple myeloma followed by repeat IMiD (thalidomide [34; 24%] or lenalidomide [106; 76%]) as one of the salvage regimens. A median of 2 treatments (range, 1-6), including a stem cell transplant in 105 patients (75%), were administered before IMiD-based salvage therapy. The median time from diagnosis to repeat exposure to IMiD was 28 months. Among the 113 evaluable patients, 50 (44%) achieved at least a partial response, and 63 (56%) achieved less than a partial response to repeat IMiD. Response rates with lenalidomide retreatment were higher than with repeat administration of thalidomide.

Combining benefits of an adrenergic and a muscarinic blocker in a single formulation - a pharmacokinetic evaluation.

A pharmacokinetic bioequivalence study was conducted in Asian subjects, to compare a fixed dose combination capsule single oral dose of alpha adrenoceptor blocker-Alfuzosin hydrochloride 10mg extended release and muscarinic antagonists-Solifenacin succinate 5mg against individually administered Xatral XL 10mg tablets (Alfuzosin) of Sanofi Synthelabo Limited, United Kingdom (UK) and Vesicare 5mg tablets (Solifenacin) of Astellas Pharma Limited, UK under fed conditions. Blood samples were collected pre-dose up to 72 h post dose for determination of plasma Alfuzosin and Solifenacin concentrations and calculation of the pharmacokinetic parameters. ANOVA was performed on the log (natural)-transformed pharmacokinetic parameters. A 90% confidence interval for the ratios of the test and reference product averages (least square means) were calculated for alfuzosin and solifenacin. The 90% confidence intervals obtained for alfuzosin for Cmax, AUC0-t and AUC0-∞ were 102.74-122.75%, 95.84-116.96% and 95.82-116.76%, respectively. The 90% confidence intervals obtained for Solifenacin for Cmax, and AUC0-72 were 89.55-97.91% and 90.47-99.38%, respectively. Based on the results, the fixed dose combination was concluded to be bioequivalent to individually administered products.

A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression.

In situations like the COVID-19 pandemic, healthcare systems are under enormous pressure as they can rapidly collapse under the burden of the crisis. Machine learning (ML) based risk models could lift the burden by identifying patients with a high risk of severe disease progression. Electronic Health Records (EHRs) provide crucial sources of information to develop these models because they rely on routinely collected healthcare data. However, EHR data is challenging for training ML models because it contains irregularly timestamped diagnosis, prescription, and procedure codes. For such data, transformer-based models are promising. We extended the previously published Med-BERT model by including age, sex, medications, quantitative clinical measures, and state information. After pre-training on approximately 988 million EHRs from 3.5 million patients, we developed models to predict Acute Respiratory Manifestations (ARM) risk using the medical history of 80,211 COVID-19 patients. Compared to Random Forests, XGBoost, and RETAIN, our transformer-based models more accurately forecast the risk of developing ARM after COVID-19 infection. We used Integrated Gradients and Bayesian networks to understand the link between the essential features of our model. Finally, we evaluated adapting our model to Austrian in-patient data. Our study highlights the promise of predictive transformer-based models for precision medicine.

MultiGML: Multimodal graph machine learning for prediction of adverse drug events.

Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources. Thus, many current computational approaches neglect a wealth of information which results from the integration of different data sources, such as biological protein function, gene expression, chemical compound structure, cell-based imaging and others. In this work we propose an integrative and explainable multi-modal Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events and general drug target-phenotype associations. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various traditional knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event. Hence, MultiGML could be a versatile tool to support decision making in preclinical drug development.

The Epilepsy Ontology: a community-based ontology tailored for semantic interoperability and text mining.

Motivation: Epilepsy is a multifaceted complex disorder that requires a precise understanding of the classification, diagnosis, treatment and disease mechanism governing it. Although scattered resources are available on epilepsy, comprehensive and structured knowledge is missing. In contemplation to promote multidisciplinary knowledge exchange and facilitate advancement in clinical management, especially in pre-clinical research, a disease-specific ontology is necessary. The presented ontology is designed to enable better interconnection between scientific community members in the epilepsy domain. Results: The Epilepsy Ontology (EPIO) is an assembly of structured knowledge on various aspects of epilepsy, developed according to Basic Formal Ontology (BFO) and Open Biological and Biomedical Ontology (OBO) Foundry principles. Concepts and definitions are collected from the latest International League against Epilepsy (ILAE) classification, domain-specific ontologies and scientific literature. This ontology consists of 1879 classes and 28 151 axioms (2171 declaration axioms, 2219 logical axioms) from several aspects of epilepsy. This ontology is intended to be used for data management and text mining purposes. Availability and implementation: The current release of the ontology is publicly available under a Creative Commons 4.0 License and shared via http://purl.obolibrary.org/obo/epso.owl and is a community-based effort assembling various facets of the complex disease. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/EPIO. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Spleen tyrosine kinase/FMS-like tyrosine kinase-3 inhibition in relapsed/refractory B-cell lymphoma, including diffuse large B-cell lymphoma: updated data with mivavotinib (TAK-659/CB-659).

We report an updated analysis from a phase I study of the spleen tyrosine kinase (SYK) and FMS-like tyrosine kinase 3 inhibitor mivavotinib, presenting data for the overall cohort of lymphoma patients, and the subgroup of patients with diffuse large B-cell lymphoma (DLBCL; including an expanded cohort not included in the initial report). Patients with relapsed/refractory lymphoma for which no standard treatment was available received mivavotinib 60-120 mg once daily in 28-day cycles until disease progression/unacceptable toxicity. A total of 124 patients with lymphoma, including 89 with DLBCL, were enrolled. Overall response rates (ORR) in response-evaluable patients were 45% (43/95) and 38% (26/69), respectively. Median duration of response was 28.1 months overall and not reached in DLBCL responders. In subgroups with DLBCL of germinal center B-cell (GCB) and non-GCB origin, ORR was 28% (11/40) and 58% (7/12), respectively. Median progression free survival was 2.0 and 1.6 months in the lymphoma and DLBCL cohorts, respectively. Grade ≥3 treatment-emergent adverse events occurred in 96% of all lymphoma patients, many of which were limited to asymptomatic laboratory abnormalities; the most common were increased amylase (29%), neutropenia (27%), and hypophosphatemia (26%). These findings support SYK as a potential therapeutic target for the treatment of patients with B-cell lymphomas, including DLBCL. Trial registration: ClinicalTrials.gov number: NCT02000934.

Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings.

Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.

Deep Learning-based detection of psychiatric attributes from German mental health records.

BACKGROUND: Health care records provide large amounts of data with real-world and longitudinal aspects, which is advantageous for predictive analyses and improvements in personalized medicine. Text-based records are a main source of information in mental health. Therefore, application of text mining to the electronic health records - especially mental state examination - is a key approach for detection of psychiatric disease phenotypes that relate to treatment outcomes. METHODS: We focused on the mental state examination (MSE) in the patients' discharge summaries as the key part of the psychiatric records. We prepared a sample of 150 text documents that we manually annotated for psychiatric attributes and symptoms. These documents were further divided into training and test sets. We designed and implemented a system to detect the psychiatric attributes automatically and linked the pathologically assessed attributes to AMDP terminology. This workflow uses a pre-trained neural network model, which is fine-tuned on the training set, and validated on the independent test set. Furthermore, a traditional NLP and rule-based component linked the recognized mentions to AMDP terminology. In a further step, we applied the system on a larger clinical dataset of 510 patients to extract their symptoms. RESULTS: The system identified the psychiatric attributes as well as their assessment (normal and pathological) and linked these entities to the AMDP terminology with an F1-score of 86% and 91% on an independent test set, respectively. CONCLUSION: The development of the current text mining system and the results highlight the feasibility of text mining methods applied to MSE in electronic mental health care reports. Our findings pave the way for the secondary use of routine data in the field of mental health, facilitating further clinical data analyses.

Phase 1 Study Evaluating Pharmacokinetics and Tolerability of Ofatumumab Combined With Bendamustine in Patients With Indolent B-Cell Non-Hodgkin's Lymphoma.

The pharmacokinetics (PK) and safety of ofatumumab and bendamustine alone and in combination were evaluated in patients with treatment-naive or relapsed indolent B-cell non-Hodgkin lymphoma (iNHL). Patients were randomly assigned to ofatumumab and bendamustine or ofatumumab alone. Ofatumumab PK concentration profiles and parameters were similar, alone or in combination with bendamustine. A decrease of 14% in the maximum observed plasma concentration (Cmax ) and 15% in the area under the plasma concentration-time curve (AUC) from time 0 to the last measurable concentration sampling time (AUClast ) was observed for ofatumumab coadministered with bendamustine, which was not considered clinically relevant. Bendamustine PK concentration profiles and parameters were similar with or without ofatumumab. The most frequent treatment-related adverse event was infusion-related reaction in 53% in the combination arm and 47% in the ofatumumab arm. No relevant drug-drug interaction was observed between ofatumumab and bendamustine. Ofatumumab alone or in combination with bendamustine had a manageable safety profile.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

Clinical safety and pharmacokinetic evaluation of aqueous extract of Cocculus hirsutus, an anti-viral phytopharmacetical drug as a potential for the treatment of dengue and COVID-19.

Background and aim: Dengue a worldwide concern for public health has no effective vaccine or drug available for its prevention or treatment. There are billions of people who are at risk of contracting the dengue virus (DENV) infections with only anti-mosquito strategies to combat this disease. Based on the reports, particularly in vitro studies and small animal studies showing anti-viral activity of aqueous extract of Cocculus hirsutus (AQCH), studies were conducted on AQCH tablets as a potential for the treatment of dengue and COVID-19 infections. The current study was part of the research on AQCH tablet formulation and was aimed to evaluate safety and pharmacokinetics in healthy human subjects. Materials and methods: Sixty healthy adult human subjects were divided into 5 groups (cohorts: I to V; n = 12 per cohort) and randomized in the ratio of 3:1 to receive active treatment or placebo in a blinded manner. Five doses 100 mg, 200 mg, 400 mg, 600 mg and 800 mg tablets were administered three times daily at an interval of 8 h for days 01-09 under fasting conditions and a single dose in morning on day 10. Safety assessment was based on monitoring the occurrence, pattern, intensity, and severity of adverse events during study period. Blood samples were collected for measurement of the bio-active marker Sinococuline concentrations by a validated LC-MS/MS method followed by pharmacokinetic evaluation. Results and conclusion: The test formulation was well tolerated in all cohorts. Sinococuline peak plasma concentration (Cmax) and total exposure of plasma concentration (AUC) demonstrated linearity up to 600 mg and saturation kinetics at 800 mg dose. There was no difference observed in elimination half-life for all the cohorts, suggesting absence of saturation in rate of elimination. Dose accumulation was observed and steady state was achieved within 3 days. The information on human pharmacokinetics of AQCH tablets would assist in further dose optimization with defined pharmacokinetic-pharmacodynamic relationship.