Information support the design of new geriatric drugs

В статье рассматриваются особенности использования информационных ресурсов при разработке новых гериатрических лекарственных средств. Показано, что информационное обеспечение разработки новых гериатрических лекарственных средств должно основываться на открытых электронных ресурсах с доступом через сеть Интернет: электронных базах хемоинформации и Интернет-сервисах прогнозирования биологической активности веществ. Сделан вывод, что предлагаемый подход позволяет значительно сократить материальные и временные затраты на поиск новых плейотропных эффектов активных фармацевтических ингредиентов и создание новых высокоэффективных низкотоксичных лекарственных средств для людей пожилого и старческого возраста.The article discusses the features of the use of information resources in developing new geriatric drugs. It is shown that the development of new information support geriatric drugs should be based on open access to electronic resources through the Internet: chemoinformatic electronic databases and Internet services predict the biological activity of substances. It is concluded that the proposed approach can significantly reduce material and time spent on the search for new pleiotropic effects of active pharmaceutical ingredients and development of new high-performance low toxicity drugs for the elderly and senile age

Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets

Впровадження інноваційної технології віртуального експерименту в освітній процес підготовки магістрів промислової фармації

Показано, що впровадження технології in silico дослідження у додипломну науково-дослідну роботу магістрантів промислової фармації дозволяє отримати суттєві переваги в порівнянні з матеріальним (прямим) експериментом за рахунок скорочення витрат часу, грошових витрат та підвищення когнітивної мотивованості студентів. Зроблено висновок, що доцільним є введення в елективну частину навчальної програми підготовки бакалаврів напряму «Фармація» та магістрів промислової фармації спецкурсів з окремих питань біо та хемоінформатики, біохімії, молекулярної фармакології, фармацевтичної розробки лікарських засобів, що підвищить ефективність освітнього процесу. Вперше узагальнено первинний досвід інтеграції елементів in silico експерименту в наукові дослідження при додипломній підготовці магістрів зі спеціальності «Технології фармацевтичних препаратів». Результати дослідження можуть бути використані при розробці професійних освітніх програм додипломної підготовки магістрів зі спеціальності «Технології фармацевтичних препаратів»

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

<p>Abstract</p> <p>Background</p> <p>The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published <it>in silico </it>method PAAS was applied for prediction of interactions between protein kinases and their substrates.</p> <p>Results</p> <p>We used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH^®database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain™ system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell.</p> <p>Conclusions</p> <p>It was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at <url>http://www.ibmc.msk.ru/PAAS/</url>.</p

Antiarrhythmic and antioxidant activity of novel pyrrolidin-2-one derivatives with adrenolytic properties

A series of novel pyrrolidin-2-one derivatives (17 compounds) with adrenolytic properties was evaluated for antiarrhythmic, electrocardiographic and antioxidant activity. Some of them displayed antiarrhythmic activity in barium chloride-induced arrhythmia and in the rat coronary artery ligation-reperfusion model, and slightly decreased the heart rate, prolonged P–Q, Q–T intervals and QRS complex. Among them, compound EP-40 (1-[2-hydroxy-3-[4-[(2-hydroxyphenyl)piperazin-1-yl]propyl]pyrrolidin-2-one showed excellent antiarrhythmic activity. This compound had significantly antioxidant effect, too. The present results suggest that the antiarrhythmic effect of compound EP-40 is related to their adrenolytic and antioxidant properties. A biological activity prediction using the PASS software shows that compound EP-35 and EP-40 can be characterized by antiischemic activity; whereas, compound EP-68, EP-70, EP-71 could be good tachycardia agents

In silico target prediction for elucidating the mode of action of herbicides including prospective validation.

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize $\textit{in silico}$ target prediction methods in the early discovery phase to suggest the MoA of a compound $\textit{via}$ data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model ("HerbiMod") is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of ≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of using applicability domains in the first place. However, performance better than 60% in balanced accuracy was achieved on the prospective testset, where all the compounds fell within the applicability domain, and which hence underlines the possibility of using target prediction also in the area of agrochemicals.BASF SE, Unilever, European Research Council (Starting Grant ERC-2013-StG-336159 MIXTURE

In silico target prediction for elucidating the mode of action of herbicides including prospective validation.

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize in silico target prediction methods in the early discovery phase to suggest the MoA of a compound via data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model ("HerbiMod") is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of using applicability domains in the first place. However, performance better than 60% in balanced accuracy was achieved on the prospective testset, where all the compounds fell within the applicability domain, and which hence underlines the possibility of using target prediction also in the area of agrochemicals.BASF SE, Unilever, European Research Council (Starting Grant ERC-2013-StG-336159 MIXTURE