The efficacy of traditional chemotherapy often suffers from rapid clearance and off-target toxicity. Drug delivery systems and controlled release are applied to improve the therapeutic efficiencies of small-molecule drugs. In this work, two novel oxidative/reductive (Ox/Re) -sensitive and one non-sensitive Paclitaxel (PTX) prodrugs were synthesized with a maleimide group, which rapidly conjugates with albumin in vivo. Albumin serves as a good vehicle to deliver more prodrug to tumors due to the enhanced permeation and retention (EPR) effect. PTX was then released from the prodrugs in glutathione(GSH)/ reactive oxygen species(ROS)-rich tumor microenvironments. This bioresponsive prodrug strategy demonstrates potent chemotherapeutic efficiency in vivo and may be utilized in clinical cancer therapy.

Purpose: The aim of this study was to analyze the blood glucose (BG) variations in cancer patients during chemotherapy according to tumor types and chemotherapeutic regimens. Materials and Methods: Patients were examined from the Department of Medical Oncology of Cancer Hospital and Institute, Chinese Academy Medical Sciences from January 2012 to December 2014. The patients' ages, genders, body mass index, associated disease history, and corresponding BG values were recorded. Results: Among these 2029 patients, 331 (16.3%) patients encountered high BG during chemotherapy except diabetic patients. Of these patients, 208 (62.8%) were males, and 123 (37.2%) were females, with age ranged from 17 to 84 years. The 331 cases included 23 tumor types and 77 regimens. Totally, BG values increased up to 7.4 ± 1.3 mmol/L during chemotherapy. Conclusions: No previous studies in the literature have examined systematically so numerous cases of hyperglycemia during chemotherapy. This study has pointed out possible high-risk chemotherapeutic regimens and tumor types, which should be paid attention to prevent the occurrence of hyperglycemia.

Purpose: Pancreatic carcinoma is the most lethal cancer, with a 5-year survival rate of <5%. Hyperglycemia is one of the severe adverse drug reactions (ADRs) in cancer treatment. The aim was to analyze the blood glucose-related ADR of antineoplastics in treating pancreatic cancer. Materials and Methods: Antineoplastic drugs were selected from Martindale-The Complete Drug Reference (36^th edition). ADR data were extracted from VigiBase, the WHO Uppsala Monitoring Centre, and the WHO's specialist center for drug safety. Results: Nineteen antineoplastic drugs were selected; VigiBase provided their ADR records including total 235,625 records and 27 heading ADR items, 1348 records of glucose metabolism disorders (GMDs), and 807 records of hyperglycemia. Based on the emphasized nine antineoplastic drugs with high hyperglycemic ADR incidence, we found: fluorouracil, sorafenib and pemetrexed with high ADR record of metabolism and nutrition disorders; fludarabine and flutamide with high ADR of GMD ratio. All the hyperglycemia ratios of the 9 antineoplastics were more than 50.0%, except pemetrexed and sorafenib. Thoroughly, doxorubicin carried high absolute records and ratios in hyperglycemic conditions. Conclusions: Pancreatic carcinoma is an aggressive malignancy typically associated with severe hyperglycemia. Furthermore, hyperglycemia is one of the severe ADRs from antineoplastics, which must be paid special attention to when treating in pancreatic carcinoma, especially doxorubicin, fluorouracil, and gemcitabine. Such real-time monitoring or pretreatment gene test can be suggested.

Introduction Mortality rates from various cancer types are higher in patients with hyperglycaemia-associated pathologies. Among antineoplastics for treating pancreatic carcinoma, doxorubicin was found to be top of the list for inducing hyperglycaemia-related adverse drug reactions. Methods Individual case safety reports of doxorubicin-induced hyperglycaemia-related adverse drug reactions (HG-ADRs) submitted during the period 2000–2017 were extracted from VigiBase^®. Factors influencing outcome seriousness in these cases were analyzed using bivariate correlation and logistical regression. Age was stratified into five groups. Results Among the 558 cases with high blood glucose included in this study, with an average patient age of 55.9 ± 14.3 years, 71.1% had serious outcomes and 10.9% died. Three factors—died, age, and gender—were all found to be significantly correlated with outcome seriousness (P < 0.01), while five factors—year reported, ADR duration, latency, treatment duration, and dosage—showed no correlation with outcome seriousness (P > 0.05). Identical conclusions were reached upon analyzing only the cases that did not involve corticosteroid use. Conclusion Age and gender are risk factors for doxorubicin-induced serious HG-ADRs; males and older patients are more likely to suffer a serious outcome following such a reaction.

Objective. Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors. Methods. In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software. Results. The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of “rbf” and “nu-regression” composition. Two key values (nu and gamma) and case number displayed high adjusted r² in curve regressions ( ). This SVM model with computable parameters greatly improved the assessing indexes (accuracy, F1 score, and kappa) as well as coequal sensitivity and the area under the curve (AUC). Conclusion. We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.