Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.

Background Nonadherence to medication in tuberculosis (TB) hampers optimal treatment outcomes. Digital health technology (DHT) seems to be a promising approach to managing problems of nonadherence to medication and improving treatment outcomes. Objective This paper systematically reviews the effect of DHT in improving medication adherence and treatment outcomes in patients with TB. Methods A literature search in PubMed and Cochrane databases was conducted. Randomized controlled trials (RCTs) that analyzed the effect of DHT interventions on medication adherence outcomes (treatment completion, treatment adherence, missed doses, and noncompleted rate) and treatment outcomes (cure rate and smear conversion) were included. Adult patients with either active or latent TB infection were included. The Jadad score was used for evaluating the study quality. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline was followed to report study findings. Results In all, 16 RCTs were selected from 552 studies found, and 6 types of DHT interventions for TB were identified: 3 RCTs examined video directly observed therapy (VDOT), 1 examined video-observed therapy (VOT), 1 examined an ingestible sensor, 1 examined phone call reminders, 2 examined medication monitor boxes, and 8 examined SMS text message reminders. The outcomes used were treatment adherence, including treatment completion, treatment adherence, missed dose, and noncompleted rate, as well as clinical outcomes, including cure rate and smear conversion. In treatment completion, 4 RCTs (VDOT, VOT, ingestible sensor, SMS reminder) found significant effects, with odds ratios and relative risks (RRs) ranging from 1.10 to 7.69. Treatment adherence was increased in 1 study by SMS reminders (RR 1.05; 95% CI 1.04-1.06), and missed dose was reduced in 1 study by a medication monitor box (mean ratio 0.58; 95% CI 0.42-0.79). In contrast, 3 RCTs of VDOT and 3 RCTs of SMS reminders did not find significant effects for treatment completion. Moreover, no improvement was found in treatment adherence in 1 RCT of VDOT, missed dose in 1 RCT of SMS reminder, and noncompleted rate in 1 RCT of a monitor box, and 2 RCTs of SMS reminders. For clinical outcomes such as cure rate, 2 RCTs reported that phone calls (RR 1.30; 95% CI 1.07-1.59) and SMS reminders (OR 2.47; 95% CI 1.13-5.43) significantly affected cure rates. However, 3 RCTs found that SMS reminders did not have a significant impact on cure rate or smear conversion. Conclusions It was found that DHT interventions can be a promising approach. However, the interventions exhibited variable effects regarding effect direction and the extent of improving TB medication adherence and clinical outcomes. Developing DHT interventions with personalized feedback is required to have a consistent and beneficial effect on medication adherence and outcomes among patients with TB.

Glutathione-S-transferases (GSTs) are enzymes involved in cellular detoxification by catalyzing the nucleophilic attack of glutathione (GSH) on the electrophilic center of numerous of toxic compounds and xenobiotics, including chemotherapeutic drugs. Human GST P1-1, which is known as the most prevalent isoform of the mammalian cytosolic GSTs, is overexpressed in many cancers and contributes to multidrug resistance by directly conjugating to chemotherapeutics. It is suggested that this resistance is related to the high expression of GST P1-1 in cancers, thereby contributing to resistance to chemotherapy. In addition, GSTs exhibit sulfonamidase activity, thereby catalyzing the GSH-mediated hydrolysis of sulfonamide bonds. Such reactions are of interest as potential tumor-directed prodrug activation strategies. Herein we report the design and synthesis of some novel sulfonamide-containing benzoxazoles, which are able to inhibit human GST P1-1. Among the tested compounds, 2-(4-chlorobenzyl)-5-(4-nitrophenylsulfonamido)benzoxazole (5 f) was found as the most active hGST P1-1 inhibitor, with an IC50 value of 10.2 μM, showing potency similar to that of the reference drug ethacrynic acid. Molecular docking studies performed with CDocker revealed that the newly synthesized 2-substituted-5-(4-nitrophenylsulfonamido)benzoxazoles act as catalytic inhibitors of hGST P1-1 by binding to the H-site and generating conjugates with GSH to form S-(4-nitrophenyl)GSH (GS-BN complex) via nucleophilic aromatic substitution reaction. The 4-nitrobenzenesulfonamido moiety at position 5 of the benzoxazole ring is essential for binding to the H-site and for the formation of the GST-mediated GSH conjugate.

A pharmacophore describes the framework of molecular features that are vital for the biological activity of a compound. Pharmacophore models are built by using the structural information about the active ligands or targets. The pharmacophore models developed are used to identify novel compounds that satisfy the pharmacophore requirements and thus expected to be biologically active. Drug discovery process is a challenging task that requires the contribution of multidisciplinary approaches. Pharmacophore modeling has been used in various stages of the drug discovery process. The major application areas are virtual screening, docking, drug target fishing, ligand profiling, and ADMET prediction. There are several pharmacophore modeling programs in use. The user must select the right program for the right purpose carefully. There are new developments in pharmacophore modeling with the involvement of the other computational methods. It has been integrated with molecular dynamics simulations. The latest computational approaches like machine learning have also played an important role in the advances achieved. Moreover, with the rapid advance in computing capacity, data storage, software and algorithms, more advances are anticipated. Pharmacophore modeling has contributed to a faster, cheaper, and more effective drug discovery process. With the integration of pharmacophore modeling with the other computational methods and advances in the latest algorithms, programs that have better perfomance are emerging. Thus, improvements in the quality of the phamacophore models generated have been achieved with this new developments.

The resistance-nodulation-division (RND) family efflux pumps are important in the antibiotic resistance of Gram-negative bacteria. However, although a number of bacterial RND efflux pump inhibitors have been developed, there has been no clinically available RND efflux pump inhibitor to date. A set of BSN-coded 2-substituted benzothiazoles were tested alone and in combinations with ciprofloxacin (CIP) against the AcrAB-TolC overexpressor Escherichia coli AG102 clinical strain. The results indicated that the BSN compounds did not show intrinsic antimicrobial activity when tested alone. However, when used in combinations with CIP, a reversal in the antibacterial activity of CIP with up to 10-fold better MIC values was observed. In order to describe the binding site features of these BSN compounds with AcrB, docking studies were performed using the CDocker method. The performed docking poses and the calculated binding energy scores revealed that the tested compounds BSN-006, BSN-023, and BSN-004 showed significant binding interactions with the phenylalanine-rich region in the distal binding site of the AcrB binding monomer. Moreover, the tested compounds BSN-006 and BSN-023 possessed stronger binding energies than CIP, verifying that BSN compounds are acting as the putative substrates of AcrB.