Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design.

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.

Expediting the Design, Discovery and Development of Anticancer Drugs using Computational Approaches.

Cancer is considered as one of the world's leading causes of morbidity and mortality. Over the past four decades, spectacular advances in molecular and cellular biology have led to major breakthroughs in the field of cancer research. However, the design and development of anticancer drugs prove to be an intricate, expensive, and time-consuming process. To overcome these limitations and manage large amounts of emerging data, computer- aided drug discovery/design (CADD) methods have been developed. Computational methods can be employed to help and design experiments, and more importantly, elucidate structure-activity relationships to drive drug discovery and lead optimization methods. Structure- and ligand-based drug designs are the most popular methods utilized in CADD. Additionally, the assimilation provided by these two complementary approaches are even more intriguing. Nowadays, the integration of experimental and computational approaches holds great promise in the rapid discovery of novel anticancer therapeutics. In this review, we aim to provide a comprehensive view on the state-of-the-art technologies for computer-assisted anticancer drug development with thriving models from literature. The limitations associated with each traditional in silico method have also been discussed, which can help the reader to rationale the best computational tool for their analysis. In addition, we will also shed some light on the latest advances in the computational approaches for anticancer drug development and conclude with a brief precis.

Acute Pancreatitis in a patient with hypercalcemia due to tertiary hyperparathyroidism.

Hypercalcemia represents an independent risk factor of acute pancreatitis and can result from hyperfunctioning parathyroid glands. Here, we report on a 35-year-old patient who was admitted to our hospital with abdominal pain six weeks after kidney transplantation. Based on laboratory tests and ultrasound imaging, acute pancreatitis with hypercalcemia due to tertiary hyperparathyroidism was diagnosed. Subsequently, the patient was treated by parathyroidectomy with autologous tissue transplantation. This constellation points to acute pancreatitis as a very rare and severe complication of patients developing tertiary hyperparathyroidism-related hypercalcemia from secondary hyperparathyroidism after kidney transplantation.