The majority of the well-known pharmacogenomics research used in the medical sciences contributes to our understanding of medication interactions. It has a significant impact on treatment and drug development. The broad use of pharmacogenomics is required for the progress of therapy. The main focus is on how genes and an intricate gene system affect the body’s reaction to medications. Novel biomarkers that help identify a patient group that is more or less likely to respond to a certain medication have been discovered as a result of recent developments in the field of clinical therapeutics. It aims to improve customized therapy by giving the appropriate drug at the right dose at the right time and making sure that the right prescriptions are issued. A combination of genetic, environmental, and patient variables that impact the pharmacokinetics and/or pharmacodynamics of medications results in interindividual variance in drug response. Drug development, illness susceptibility, and treatment efficacy are all impacted by pharmacogenomics. The purpose of this work is to give a review that might serve as a foundation for the creation of new pharmacogenomics applications, techniques, or strategies.

Despite the advanced development in the field of drug discovery and design, fighting infectious and non-infectious diseases remains a major worldwide heath challenge due to the limited activity of currently used drugs. Nevertheless, in recent years, the approach of designing nanoparticles for therapeutic applications has gained more interest and promise for future use. Thus, the current study is focused on the evaluation of A. judaica extract and chitosan nanoparticles loaded extract (CNPsLE) for potential antimicrobial and anticancer activities. The HPLC analysis of the extract has shown the presence of various phenolic and flavonoid compounds, including kaempferol (3916.34 µg/mL), apigenin (3794.32 µg/mL), chlorogenic acid (1089.58 µg/mL), quercetin (714.97 µg/mL), vanillin (691.55 µg/mL), naringenin (202.14 µg/mL), and rutin (55.64 µg/mL). The extract alone showed higher MIC values against B. subtilis, E. coli, S. aureus, K. pneumonia, and C. albicans (62.5, 15.65, 15.62, 31.25, and 31.25 µg/mL, respectively), whereas lower MIC values were observed when the extract was combined with CNPsLE (0.97, 1.95, 3.9, 4.1, and 15.62 µg/mL, respectively). The extract exhibited low cytotoxicity against normal Vero cells with IC₅₀ 173.74 µg/mL in comparison with the cytotoxicity of the CNPsLE (IC₅₀, 73.89 µg/mL). However, CNPsLE showed more selective toxicity against the human prostate cancer cell line (PC3) with IC₅₀ of 20.8 µg/mL than the extract alone with 76.09 µg/mL. In the docking experiments, kaempferol and apigenin were revealed to be suitable inhibitors for prostate cancer (2Q7L). Overall, the obtained data highlighted the promising potential therapeutic use of CNPsLE as an anticancer and antimicrobial agent.

It is worth noting that laurel (Laurus nobilis L.) contains several pharmacologically and nutritionally active compounds that may differ according to the pretreatment process. The current study is designed to clarify the effect of moist heat on the phenolic and flavonoid constituents and anti-Helicobacter pylori, antioxidant, antidiabetic, and anti-Alzheimer’s activities of laurel leaf extract (LLE). Unmoist-heated (UMH) and moist-heated (MH) LLEs showed the presence of numerous flavonoid and phenolic constituents, although at different levels of concentration. MH significantly induced (p < 0.05) the occurrence of most compounds at high concentrations of 5655.89 µg/mL, 3967.65 µg/mL, 224.80 µg/mL, 887.83 µg/mL, 2979.14 µg/mL, 203.02 µg/mL, 284.65 µg/mL, 1893.66 µg/mL, and 187.88 µg/mL, unlike the detection at low concentrations of 3461.19 µg/mL, 196.96 µg/mL, 664.12 µg/mL, 2835.09 µg/mL, 153.26 µg/mL, 254.43 µg/mL, 1605.00 µg/mL, 4486.02 µg/mL, and 195.60 µg/mL using UMH, for naringenin, methyl gallate, caffeic acid, rutin, ellagic acid, coumaric acid, vanillin, ferulic acid, and hesperetin, respectively. Chlorogenic acid, syringic acid, and daidzein were detected in the UMH LLE but not in the MH LLE, unlike pyrocatechol. The anti-H. pylori activity of the UMH LLE was lower (23.67 ± 0.58 mm of inhibition zone) than that of the MH LLE (26.00 ± 0.0 mm of inhibition zone). Moreover, the values of MIC and MBC associated with the MH LLE were very low compared to those of the UMH LLE. Via MBC/MIC index calculation, the UMH and MH LLEs showed cidal activity. The MH LLE exhibited higher anti-biofilm activity (93.73%) compared to the anti-biofilm activity (87.75%) of the MH LLE against H. pylori. The urease inhibition percentage was more affected in the UMH LLE compared to the MH LLE, with significant (p < 0.05) IC₅₀ values of 34.17 µg/mL and 91.11 µg/mL, respectively. Promising antioxidant activity was documented with a very low value of IC₅₀ (3.45 µg/mL) for the MH LLE compared to the IC₅₀ value of 4.69 µg/mL for the UMH LLE and the IC₅₀ value of 4.43 µg/mL for ascorbic acid. The MH LLE showed significantly higher (p < 0.05) inhibition of α-glucosidase and butyrylcholinesterase activities, with IC₅₀ values of 9.9 µg/mL and 17.3 µg/mL, respectively, compared to those of the UMH LLE at 18.36 µg/mL and 28.92 µg/mL. The molecular docking of naringenin showed good docking scores against acetylcholinesterase 1E66 and butyrylcholinesterase 6EMI, indicating that naringenin is an intriguing candidate for additional research as a possible medication for Alzheimer’s disease.

There are millions of cancer cases worldwide every year, and breast cancer is one of the most prevalent diseases with the highest mortality rate. The manual effort of pathologists can be significantly reduced by computerized diagnostic systems, which improve the accuracy and reliability of diagnosis. In this paper, we present four novel systems for breast cancer diagnosis in four different scenarios: binary versus multi-class classification and magnification-specific (MS) versus magnification-independent (MI) classification. In each of the proposed systems, we developed an automatic score-level fused CNN model using a pretrained deep neural network and named it the Multi-Level Feature Fusion (MLF2) model. The MLF2-CNN, similar to the conventional CNN models, integrates the feature extraction and classification phases of BC classification into a single automatic learning procedure. Additionally, MLF2-CNN performs an automatic score-level fusion of several classifiers that were trained with multi-level features to make the final decision. A pretrained DenseNet-121 is selected as the backbone of the proposed MLF2-CNN, and several new links are added to the CNN architecture to capture multi-stage features. Several experiments on the publicly available BreakHis dataset demonstrate that the proposed systems capture the best descriptive features and outperform state-of-the-art techniques in most of the scenarios.

Overexpression of the epidermal growth factor receptor (EGFR) has been shown to be a critical factor in tumor development and cancer progression. Although established EGFR inhibitors have been effective in the treatment of cancer, they are associated with several side effects. As a result, there is an urgent need to develop novel EGFR inhibitors that can effectively target the receptor while causing no adverse side effects. Here, the bioactive compounds of Glycyrrhiza glabra and established EGFR inhibitors have been screened against the EGFR catalytic site. The compounds LTS0058805, LTS0114552, LTS0128805, LTS0174203, LTS0007447, and LTS0164690 exhibited binding energies to the EGFR that were comparable to those of established EGFR inhibitors. Further, these hit compounds were observed to interact with critical residues of the EGFR, suggesting their potential as inhibitors of the receptor. In addition, these hits possess good drug-like properties and merit further exploration for their potential application in cancer management.

The current study was designed to formulate ternary solid dispersions (TSDs) of dexibuprofen (Dex) by solvent evaporation to augment the solubility and dissolution profile, in turn providing gastric protection and effective anti-inflammatory activity. Initially, nine formulations (S1 to S9) of binary solid dispersions (BSDs) were developed. Formulation S1 comprising a 1:1 weight ratio of Dex and Syloid 244FP^® was chosen as the optimum BSD formulation due to its better solubility profile. Afterward, 20 TSD formulations were developed using the optimum BSD. The formulation containing Syloid 244FP^® with 40% Gelucire 48/16^® (S18) and Poloxamer 188^® (S23) successfully enhanced the solubility by 28.23 and 38.02 times, respectively, in pH 6.8, while dissolution was increased by 1.99- and 2.01-fold during the first 5 min as compared to pure drug. The in vivo gastroprotective study in rats suggested that the average gastric lesion index was in the order of pure Dex (8.33 ± 2.02) > S1 (7 ± 1.32) > S18 (2.17 ± 1.61) > S23 (1.83 ± 1.04) > control (0). The in vivo anti-inflammatory study in rats revealed that the percentage inhibition of swelling was in the order of S23 (71.47 ± 2.16) > S18 (64.8 ± 3.79) > S1 (54.14 ± 6.78) > pure drug (18.43 ± 2.21) > control (1.18 ± 0.64) after 6 h. ELISA results further confirmed the anti-inflammatory potential of the developed formulation, where low levels of IL-6 and TNF alpha were reported for animals treated with S23. Therefore, S23 could be considered an effective formulation that not only enhanced the solubility and bioavailability but also reduced the gastric irritation of Dex.