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We investigate thiazolyl-pyrazoline derivatives as promising drugs for the anti-malarial. Protein kinase G is a primary target for treating malaria due to its essential role in Plasmodium falciparu...

Spirulina platensis contain variety of pigments, such as chlorophylls, carotenoids, and phycocyanin. Phycocyanin (PC) exists in abundance, but due to its instability, the utilization of this pigment is still very limited. In this study, the PC was modified using formaldehyde crosslinks yielding phycocyanin-formaldehyde (PC-F), and its photostability was evaluated. The PC-F formation was designated by the distinctive alterations of the maximum absorption to 611 nm, which was 10 nm blue-shifted than those of the PC. Additionally, the sharp peaks of FTIR spectra at 1636 nm for C O and at 1019 nm for C O C, suggesting the interaction of phycocyanin with formaldehyde. The PC-F showed stabilization improvement up to 1.53-folds after 300 mins of yellow light exposure than those of PC. Contrary to yellow light irradiation, a severe decrease of PC-F absorbance was observed reach to 4.9-folds under UV-B irradiation. The poor stability of PC-F upon white light and UV-A irradiation were indicated by the decline of PC-F absorbance up to 1.72 and 1.80, respectively. Moreover, the present study suggests that the modification of phycocyanin by formaldehyde crosslink can increase photostability upon yellow light irradiation.

Dengue fever is a disease transmitted by infected mosquitoes. This disease spreads in several countries, especially those with a tropical climate. To date, there is no specific drug that can be used to treat dengue. Use of clinically investigated drugs, such as Balapiravir, is still not effective in inhibiting the activity of virus replication. The design of a drug candidate can be performed by using the non-structural protein 3 (NS3) as target. This study aimed to develop QSAR models to predict the inhibitory activity class of NS3 inhibitors. The classification was performed by using feature importance analysis for selecting the descriptors and three ensemble methods, i.e. random forest (RF), adaptive boosting (AdaBoost), and extremely randomized trees (ERT), for model design and prediction. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that model 9, developed from ERT produced the best performance with values of accuracy and AUC equal to 0.73 and 0.82, respectively. Use of y-scrambling method allowed us to confirm that the model was not related to the chance correlation.

Malaria is a disease caused by the Plasmodium falciparum parasite and leads to many cases of deaths. Recently, the combination of several drugs has been used to treat this disease. However, the parasite is known to be resistant to the anti-malarial agent. Hence, a new candidate for an anti-malarial drug is required to solve the resistance problem. One compound that is promising as an anti-malarial agent is fusidic acid derivatives. Fusidic acid is an antibiotic that is work by preventing parasite growth. Besides, fusidic acid is known to have antiplasmodial activity although the IC50 is still poor. However, the activity can be improved by optimizing the structure through its derivatives. In this study, we developed a QSAR model to predict the activity of fusidic acid derivatives as anti-malarial agent. The model was developed by using Simulated Annealing (SA) for feature selection and Support Vector Machine (SVM) for model development. The results show that SA produces a satisfying combination of features that are indicated by the trend of MSE value during the selection process. Regarding the performance, SVM with RBF kernel produces the best result of the validation parameter. This indicates that the model is valid to be used to predict a compound with unknown activity values for anti-malarial agents.

In the last decade, Bandung has become one of the tourism destination places in Indonesia. It is reported that almost 6.7 million visitors come to Bandung in 2018, and the number increased by almost 4% per year since 2014. This rise in the number of visitors leads to the establishment of several toll gates as main access points throughout the city. As one of the busiest ones, Buah Batu toll gate is frequently congested because of the location that is close to the southern part of Bandung. To overcome this problem, a traffic regulation based on computer simulation is urgently required. In this study, we simulate the traffic system on the Buah Batu toll gate by using a combination of Nagel-Schreckenberg (NaSch) and Daoudia and Moussa (DM) models. NaSch model was used to defined vehicle movement, while the DM model was used to allow a vehicle to change lane. We defined three scenarios to evaluate the effectivity of the closing gate scheme. We found that the closing of gate 5 is more effective than the closing of gate 1. We also investigated the contribution of traffic density and driver’s behavior, e.g., stopping behavior and lane-changing behavior, to the average velocity of the vehicles.

Human immunodeficiency virus (HIV) is a virus that infects an immune cell and makes the patient more susceptible to infections and other diseases. HIV is also a factor that leads to acquired immune deficiency syndrome (AIDS) disease. The active target that is usually used in the treatment of HIV is HIV-1 protease. Combining HIV-1 protease inhibitors and reverse-transcriptase inhibitors in highly active antiretroviral therapy (HAART) is typically used to treat this virus. However, this treatment can only reduce the viral load, restore some parts of the immune system, and failed to overcome the drug resistance. This study aimed to build a QSAR model for predicting HIV-1 protease inhibitor activity using the gravitational search algorithm-neural network (GSA-NN) method. The GSA method is used to select molecular descriptors, while NN was used to develop the prediction model. The improvement of model performance was found after performing the hyperparameter tuning procedure. The validation results show that model 3, containing seven descriptors, shows the best performance indicated by the coefficient of determination (r2) and cross-validation coefficient of determination (Q2) values. We found that the value of r2 for train and test data are 0.84 and 0.82, respectively, and the value of Q2 is 0.81.

Phycocyanin (PC) is the main pigment found in Spirulina platensis and has the potential effect to treat effectively type-2 diabetes mellitus by inhibiting α-amylase and α-glucosidase. However, studies on molecular interactions between PC with α-amylase and α-glucosidase enzymes are still rare. In this study, an in-silico study was carried out to predict the molecular interactions between PC with α-amylase and α-glucosidase enzymes. Molecular docking simulations indicated that PC inhibits the enzymes by binding to the active site and causing a disruption on substrate-enzyme binding. In both enzymes, PC seem to play a crucial role in establishing the interaction within the cavity of active sites. This result suggested PC as a potential candidate for antidiabetic natural therapeutic agents. An in-vitro inhibition activity test showed that PC inhibits human salivary amylase at average of 51.13 %. A storage stability tests showed that keeping PC in solid-state, absence of lights and low temperature can preserve the bioactivity when used as functional compounds. Taken together, this current result would be useful in elucidating the molecular mechanisms of the interaction between PC and carbohydrate-metabolisms enzymes and contribute to making full use of PC as antidiabetic drug or therapeutic agent. Further confirm on diabetic subjects is indispensable to provide the potential therapeutic of PC as an effective anti-diabetic with less frequent of side effect.