In silico fragment-based drug design using a PASS approach

Biological Activity of Hexaazaisowurtzitane Derivatives

Biologically active compounds of natural or synthetic origin have a complex structure and generally contain various structural groups among which polycyclic cage amines are found. Hexaazaisowurtzitanes are representatives of these amines and studies on their biological activity began less than two decades ago, starting with research on the environmental impact of CL-20. This research helped to evaluate the risks of potential pollution in the habitat environments of living organisms and determine whether the chemical compounds in question could be utilized in pesticides, herbicides, fungicides, or medicinal drugs. The nomenclature of hexaazaisowurtzitane compounds has recently been expanded significantly, and some of them have demonstrated promise in the design of medicinal drugs. This paper review studies the pharmacological activity of the acyl derivatives of hexaazaisowurtzitane. Most of the compounds have been found to possess a high analgesic activity, providing a solution to the pressing issue of pain management in current pharmacology. Analgesic drugs currently used in the clinical practice do not meet all of the efficacy and safety requirements (gastro-, nephro-, hepato-, haematotoxicity, etc.). The material presented in the seven sections of this paper highlights information about hexaazaisowurtzitane derivatives. Furthermore, they have been observed to exhibit anti-inflammatory, anticonvulsant, antihypoxic, and antimetastatic activities, which render them highly promising for evaluation in various fields of medicinal practice.

A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy

Human Immunodeficiency Virus Type 1 (HIV-1) infection is associated with high mortality if no therapy is provided. Currently, the treatment of an HIV-1 positive patient requires that several drugs should be taken simultaneously. The resistance of the virus to an antiretroviral drug may lead to treatment failure. Our approach focuses on predicting the exposure of a particular viral variant to an antiretroviral drug or drug combination. It also aims at the prediction of drug treatment success or failure. We utilized nucleotide sequences of HIV-1 encoding protease and reverse transcriptase to perform such types of prediction. The PASS (Prediction of Activity Spectra for Substances) algorithm based on the naive Bayesian classifier was used to make a prediction. We calculated the probability of whether a sequence belonged (P₁) or did not belong (P₀) to the class associated with exposure of the viral sequence to the set of drugs that can be associated with resistance to the set of drugs. The accuracy calculated as the average Area Under the ROC (Receiver Operating Characteristic) Curve (AUC/ROC) for classifying exposure of the sequence to the HIV-1 protease inhibitors was 0.81 (±0.07), and for HIV-1 reverse transcriptase, it was 0.83 (±0.07). To predict cases of treatment effectiveness or failure, we used P₁ and P₀ values, obtained in PASS, along with the binary vector constructed based on short nucleotide descriptors and the applied random forest classifier. Average AUC/ROC prediction accuracy for the prediction of treatment effectiveness or failure for the combinations of HIV-1 protease inhibitors was 0.82 (±0.06) and of HIV-1 reverse transcriptase was 0.76 (±0.09).