Dinesh Yugraj Gawande

Noise-induced hearing loss (NIHL) is a leading cause of permanent hearing impairment worldwide, yet no pharmacological therapies are currently available to prevent or treat this disorder. Although inflammation is increasingly recognized as a key contributor to cochlear degeneration, the therapeutic potential of targeting early inflammatory signaling remains poorly understood. Here, we combined phenotypic screening in zebrafish with mechanistic and functional validation in complementary mouse models to identify quinoxaline derivatives with otoprotective activity following acoustic trauma. Lead compounds preserved cochlear synapses and auditory function after moderate noise exposure, while one derivative also protected sensory hair cells in a model of permanent hearing loss. Mechanistic analyses demonstrated that this protection was associated with attenuation of early NF-κB signaling and modulation of the cochlear inflammatory response toward a reparative state, consistent with suppression of pathogenic innate immune activation before irreversible tissue damage occurred. Together, these findings identify early NF-κB-dependent inflammatory signaling as a therapeutically actionable mechanism in NIHL and establish quinoxaline derivatives as promising candidates for pharmacological intervention. More broadly, this work demonstrates the utility of a cross-species discovery platform for identifying therapies that preserve sensory function by targeting early inflammatory pathways.

Applicability of our computer programs PASS and PharmaExpert for prediction of biological activity spectra of rather complex and structurally diverse phytocomponents of medicinal plants, both...

In silico techniques in drug discovery may rationalise and speed up the identification of lead molecules from nature. Drug discovery from medicinal plants has mostly been confined to indications in accordance with their ethnical use only. However, the availability of multiple phytoconstituents in medicinal plants suggests that these may be much more useful beyond their traditional uses and in the management of chronic diseases, along with their comorbidities. In this study, the computer programmes PASS and PharmaExpert were used to reveal the medicinal plants useful in the comprehensive management of epilepsy and associated psychiatric disorders based on the pleiotropic effects predicted for their phytoconstituents. In silico analysis revealed that seven of 50 medicinal plants from traditional Indian medicine possessed the desired pleiotropic effect, i.e., anticonvulsant, antidepressant, and nootropic activities. The majority of phytoconstituents from Passiflora incarnata were concurrently predicted to have the desired pleiotropic effects. Therefore, P. incarnata was pharmacologically validated using the pentylenetetrazole kindling mouse model. Behavioural and neurochemical evaluations confirmed the ameliorative role of P. incarnata in epilepsy and the associated depression and memory deficit. The pharmacological findings from this study propose that PASS and PharmaExpert may serve as good tools for the optimisation of the selection of plants based on their phytoconstituents for the treatment of different ailments, even beyond their traditional use.

Applicability of our computer programs PASS and PharmaExpert to prediction of biological activity spectra of rather complex and structurally diverse phytocomponents of medicinal plants, both separately and in combinations has been evaluated. The web-resource on phytochemicals of 50 medicinal plants used in Ayurveda was created for the study of hidden therapeutic potential of Traditional Indian Medicine (TIM) (http://ayurveda.pharmaexpert.ru). It contains information on 50 medicinal plants, their using in TIM and their pharmacology activities, also as 1906 phytocomponents. PASS training set was updated by addition of information about 946 natural compounds; then the training procedure and validation were performed, to estimate the quality of PASS prediction. It was shown that the difference between the average accuracy of prediction obtained in leave-5%-out cross-validation (94,467%) and in leave-one-out cross-validation (94,605%) is very small. These results showed high predictive ability of the program. Results of biological activity spectra prediction for all phytocomponents included in our database are in good correspondence with the experimental data. Additional kinds of biological activity predicted with high probability provide the information about most promising directions of further studies. The analysis of prediction results of sets of phytocomponents in each of 50 medicinal plants was made by PharmaExpert software. Based on this analysis, we found that the combination of phytocomponents from Passiflora incarnata may exhibit nootropic, anticonvulsant and antidepressant effects. Experiments carried out in mice models confirmed the predicted effects of Passiflora incarnata extracts. Исследована применимость разработанных нами компьютерных программ PASS и PharmaExpert к анализу спектров биологической активности достаточно сложных и разнообразных по структуре фитокомпонентов лекарственных растений, как по-отдельности, так и в комбинациях. С этой целью была создана база данных, содержащая известную информацию о структурных формулах и биологической активности 1906 фитокомпонентов 50 лекарственных растений из традиционной индийской медицины (ТИМ) Аюрведа (ayurveda.pharmaexpert.ru). Обучающая выборка программы PASS была пополнена информацией о структуре и биологической активности 946 природных соединений; проведены обучение и валидация, позволившие оценить качество прогноза PASS. Показано, что различия между значениями средней точности прогноза при кросс-валидации с разбиением выборки на 20 частей (94,467%) и при скользящем контроле с исключением по одному (94,605%) незначительны, что свидетельствует о хорошей прогностической способности программы. Результаты прогноза спектров биологической активности для всех включенных в нашу базу данных фитокомпонентов в 83,5% случаев совпали с известными экспериментальными данными. Предсказанные с высокой вероятностью дополнительные виды активности указывают на перспективные направления дальнейших исследований отдельных фитокомпонентов лекарственных растений. С помощью компьютерной программы PharmaExpert мы выполнили анализ результатов прогноза для комбинаций фитокомпонентов, содержащихся в отдельных лекарственных растениях ТИМ. На основе этого анализа установлено, что комбинация фитокомпонентов растения Passiflora incarnata может обладать ноотропным, противосудорожным и антидепрессантным эффектами. Полученные в экспериментах на мышиных моделях результаты подтвердили наличие прогнозируемых видов биологической активности у экстрактов Passiflora incarnata.

Covering: up to 2014 In silico approaches have been widely recognised to be useful for drug discovery. Here, we consider the significance of available databases of medicinal plants and chemo- and bioinformatics tools for in silico drug discovery beyond the traditional use of folk medicines. This review contains a practical example of the application of combined chemo- and bioinformatics methods to study pleiotropic therapeutic effects (known and novel) of 50 medicinal plants from Traditional Indian Medicine.