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Background: Sound drug safety information is important to optimize patient management, but the widely recognized comprehensive landscape of culprit-drugs that cause severe cutaneous adverse reactions (SCARs) is currently lacking.Objective: The main aim of the study is to provide a comprehensive landscape of culprit-drugs for SCARs to guide clinical practice.Methods: We analyzed reports associated with SCARs in the FDA Adverse Event Reporting System database between 1 January 2004 and 31 December 2021 and compiled a list of drugs with potentially serious skin toxicity. According to this list, we summarized the reporting proportions of different drugs and drug classes and conducted disproportionality analysis for all the drugs. In addition, the risk characteristic of SCARs due to different drugs and drug classes was summarized by the positive–negative distribution based on the results of the disproportionality analysis.Results: A total of 77,789 reports in the FDA Adverse Event Reporting System database were considered SCAR-related, of which lamotrigine (6.2%) was the most reported single drug followed by acetaminophen (5.8%) and allopurinol (5.8%) and antibacterials (20.6%) was the most reported drug class followed by antiepileptics (16.7%) and antineoplastics (11.3%). A total of 1,219 drugs were reported as culprit-drugs causing SCARs in those reports, and the largest number of drugs belonged to antineoplastics. In disproportionality analysis, 776 drugs showed at least one positive pharmacovigilance signal. Drugs with the most positive signals were lamotrigine, acetaminophen, furosemide, and sulfamethoxazole/trimethoprim.Conclusion: Our study provided a real-world overview of SCARs to drugs, and the investigation of SCAR positive–negative distribution across different drugs revealed its risk characteristics, which may help optimize patient management.

Background and aim: Infectious disease (ID) consultation can improve multidrug-resistant organism (MDRO) treatment outcomes. However, the impact of clinical pharmacists’ ID consultation on MDRO therapy, especially early initiation, has not been reported. In this study, we try to explore the impact of the pharmacist early active consultation (PEAC) on MDRO patient management.Methods: We conducted a prospective historical controlled study based on PEAC in MDRO patients. The retrospective control group was patients hospitalized 18 months before the PEAC initiation, and the prospective PEAC group was patients hospitalized 18 months after the PEAC initiation. Primary endpoint was 30-day all-cause mortality. Secondary outcomes were MDRO clinical outcome, duration of antibiotic use, length of stay, antibiotic consumption and antibiotic costs. Further subgroup analysis of secondary outcomes was performed by the condition at admission, MDRO pathogenicity and MDRO clinical outcome.Results: 188 MDRO patients were included. After adjusting for potential predictors, PEAC reduced the 30-day all-cause mortality by 70% (HR 0.30, 95% CI 0.09–0.96, p = 0.042). PEAC group had clinical improvement than control group (89.47% vs. 65.59%, p < 0.001), especially in patients with non-severe clinical conditions at admission (98.41% vs. 70.18%, p < 0.001). However, no significant differences were found between groups in length of stay, antibiotics consumption, and antibiotics costs.Conclusion: Early active pharmacy ID consultation can reduce 30-day all-cause mortality and improve clinical outcomes in MDRO patients.

Background & Aims: Immune checkpoint inhibitors (ICIs) have transformed the landscape of cancer treatment, and ICI-related toxicities (i.e., immune-related adverse events (irAEs) have been reported in many clinical studies. However, the toxicity data of real-world have not been fully assessed.Methods: Patients with histologically confirmed solid tumors who had been treated with PD-1 inhibitors were included in the study. Patient data were collected from electronic medical records, including basic characteristics, data of irAEs, management and outcome. Incidences of irAEs were pooled and compared, and the risk of irAEs was also analyzed.Results: A total of 362 solid tumor patients treated with sintilimab (n = 171), camrelizumab (n = 60), toripalimab (n = 72), and pembrolizumab (n = 59) were included. In total, any grade irAEs, grade 1–2 irAEs, and grade ≥3 irAEs accounted for 47.24%, 38.67% and 8.56% of cases, reapectively. Further, 29.24% of patients discontinued immunotherapy due to irAEs, with pneumonitis being the main reason for discontinuation. By comparing the toxicity profiles between different ICIs, we found that reactive capillary haemangiomas were camrelizumab-specific. Additionally, the frequency of irAEs was association with ICIs type, the pooled incidence (standardized rate) of irAEs related to sintilimab, camrelizumab, toripalimab and pembrolizumab were 55.56% (52.81%), 48.33% (55.55%), 33.33% (29.23%) and 38.98% (38.29%), respectively. Sintilimab and camrelizumab had higher incidences of any grade and grade 1–2 than toripalimab (55.56% vs. 33.33%, p = 0.002; 48.54% vs. 25.00%, p = 0.0001) and pembrolizumab (55.56% vs. 38.98%, p = 0.0028; 48.54% vs. 25.42%, p = 0.002), while the grade ≥3 irAEs of pembrolizumab (13.56%) were approximately 1.63- to 1.93-fold higher than other ICIs, and the standardized grade ≥3 of pembrolizumab was significantly higher than that of sintilimab (13.21% vs. 7.12%, p = 0.026), especially for grade ≥3 pneumonitis. Multivariate analysis found that cumulative cycles of ICI (OR = 1.081; 95% CI: 1.023–1.142; p = 0.006), and lung cancer (OR = 1.765; 95% CI: 1.105–2.820; p = 0.017) were independent risk factors for irAEs.Conclusion: The frequency of irAEs is associated with ICI type. The pooled incidence of irAEs related to sintilimab and pneumonitis caused by pembrolizumab were higher. These data indicate the importance of having different monitoring priorities for different PD-1 inhibitors.

The utility of pharmacist consultation for drug-induced liver injury (DILI) management has not been explored. This retrospective cohort study evaluated the impact of a pharmacist active consultation (PAC) service on the management and outcome in patients with DILI. Consecutive patients meeting clinical biochemical criteria for DILI were enrolled at a tertiary teaching hospital between 1 January 2020 and 30 April 2022. The Roussel Uclaf Causality Assessment Method was used to assess causality between drug use and liver injury for each suspected DILI patient. Included patients were grouped according to whether they received PAC, and a proportional hazard model with multivariate risk adjustment, inverse probability of treatment weighting (IPTW), and propensity score matching (PSM) was used to assess DILI recovery. In the PSM cohort, the quality of medical care was compared between PAC and no PAC groups. A total of 224 patients with DILI (108 who received PAC and 116 who did not) were included in the analysis. Of these patients, 11 (10%) were classified as highly probable, 58 (54%) as probable, and 39 (36%) as possible DILI in the PAC group, while six patients (5%) were classified as highly probable, 53 (46%) as probable, and 57 (49%) as possible DILI in the no PAC group (p = 0.089). During patient recovery, PAC was associated with a ∼10% increase in the cumulative 180-day recovery rate. The PAC group had a crude hazard ratio (HR) of 1.73 [95% confidence interval (CI): 1.23–2.43, p = 0.001] for DILI 180-day recovery, which remained stable after multivariate risk adjustment (HR = 1.74, 95% CI: 1.21–2.49, p = 0.003), IPTW (HR = 1.72, 95% CI: 1.19–2.47, p = 0.003), and PSM (HR = 1.49, 95% CI: 1.01–2.23, p = 0.046). In the PSM cohort, PAC was more likely to identify suspect drugs (90% vs. 60%, p < 0.001) and lead to timely withdrawal of the medication (89% vs. 57%, p < 0.001). Thus, PAC is associated with a better quality of medical care for patients with DILI and can improve patient outcomes.

Triple-negative breast cancer (TNBC) has a poor prognosis due to the lack of specific therapeutic targets. CCT020312, a selective eukaryotic translation initiation factor 2 alpha (eIF2α)/protein kinase RNA-like endoplasmic reticulum kinase (PERK) activator, may have a potent anti-tumor effect. In the present study, we examined the effects of CCT020312 on TNBC and explored the underlying mechanism. We found that CCT020312 inhibited the viability of TNBC cell lines, MDA-MB-453 and CAL-148, by inducing apoptosis and G1 phase cell cycle arrest. CCT020312 decreased the protein levels of cyclin-dependent kinase 4 (CDK4), CDK6, cyclin D1, and B-cell lymphoma 2 (Bcl-2) and increased the levels of Bcl-2-associated X protein (Bax) and cleaved poly (ADP-ribose) polymerase (PARP) compared with those in the control. CCT020312 activated PERK/eIF2α/activating transcription factor 4 (ATF4)/CCAAT-enhancer binding protein (C/EBP) homologous protein transcription factor (CHOP) signaling and inhibited protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling. Furthermore, CCT020312 inhibited tumor growth in an MDA-MB-453 orthotopic xenograft mouse model by activating the PERK/eIF2α/ATF4/CHOP pathway and inhibiting the AKT/mTOR pathway. Thus, our study shows that CCT020312 may be a potential drug candidate for TNBC treatment.

Glioma is the most common type of malignant brain tumor. Due to its highly aggressive and metastatic features, glioma is associated with poor prognosis and a lack of effective treatments. Eriodictyol, a natural flavonoid compound, has been reported to possess anti-inflammatory and antioxidant effects. However, the anti-tumor effects of eriodictyol and the underlying mechanisms have rarely been reported. In this study, we found that eriodictyol has anti-tumor activity in lung, colon, breast, pancreas, and liver cancer, and most significantly in glioma cell lines. Eriodictyol dose- and time-dependently suppresses cell proliferation, migration, and invasion in U87MG and CHG-5 glioma cells. In addition, eriodictyol induces apoptosis in U87MG and CHG-5 cells, as evaluated by flow cytometry, immunofluorescence, and Western blot. Furthermore, eriodictyol downregulates the phosphoinositide 3-kinase (PI3K)/Akt/NF-κB signaling pathway in a concentration-dependent manner. Moreover, the effects of eriodictyol on the apoptosis of glioma cells are enhanced by LY294002 (a PI3K inhibitor) and reversed by 740 Y-P (a PI3K agonist). In a mouse xenograft model, eriodictyol not only dramatically suppressed tumor growth but also induced apoptosis in tumor cells. In summary, our data illustrate that eriodictyol effectively inhibits proliferation and metastasis and induces apoptosis of glioma cell lines, which might be a result of the blockade of the PI3K/Akt/NF-κB signaling pathway.

Over 50% cancer bears TP53 mutation, the highly stabilized mutant p53 protein drives the tumorigenesis and progression. Mutation of p53 not only cause loss-of-function and dominant-negative effects (DNE), but also results in the abnormal stability by the regulation of the ubiquitin-proteasome system and molecular chaperones that promote tumorigenesis through gain-of-function effects. The accumulation of mutant p53 is mainly regulated by molecular chaperones, including Hsp40, Hsp70, Hsp90 and other biomolecules such as TRIM21, BAG2 and Stat3. In addition, mutant p53 forms prion-like aggregates or complexes with other protein molecules and result in the accumulation of mutant p53 in tumor cells. Depleting mutant p53 has become one of the strategies to target mutant p53. This review will focus on the mechanism of mutant p53 stabilization and discuss how the strategies to manipulate these interconnected processes for cancer therapy.

Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.