Risk Factors for Doxorubicin-Induced Serious Hyperglycaemia-Related Adverse Drug Reactions

Drug-induced mitochondrial toxicity: Risks of developing glucose handling impairments

Mitochondrial impairment has been associated with the development of insulin resistance, the hallmark of type 2 diabetes mellitus (T2DM). However, the relationship between mitochondrial impairment and insulin resistance is not fully elucidated due to insufficient evidence to support the hypothesis. Insulin resistance and insulin deficiency are both characterised by excessive production of reactive oxygen species and mitochondrial coupling. Compelling evidence states that improving the function of the mitochondria may provide a positive therapeutic tool for improving insulin sensitivity. There has been a rapid increase in reports of the toxic effects of drugs and pollutants on the mitochondria in recent decades, interestingly correlating with an increase in insulin resistance prevalence. A variety of drug classes have been reported to potentially induce toxicity in the mitochondria leading to skeletal muscle, liver, central nervous system, and kidney injury. With the increase in diabetes prevalence and mitochondrial toxicity, it is therefore imperative to understand how mitochondrial toxicological agents can potentially compromise insulin sensitivity. This review article aims to explore and summarise the correlation between potential mitochondrial dysfunction caused by selected pharmacological agents and its effect on insulin signalling and glucose handling. Additionally, this review highlights the necessity for further studies aimed to understand drug-induced mitochondrial toxicity and the development of insulin resistance.

Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors

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 (nu = 0.5649 × e^{(− (case/6984))}, gamma = 9.005 × 10⁻⁴ × case − 4.877 × 10⁻⁸ × case²). 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.

Risk factors and early cardiovascular outcomes in cancer patients treated with anthracycline-based chemotherapy in Tanzania: a protocol for a quasi-experimental study

The objective of this quasi-experimental study is to assess the risk factors and early cardiovascular outcomes in cancer patients treated with anthracycline-based chemotherapy in Tanzania. The study will be conducted at Ocean Road Cancer Institute in Dar es salaam, Tanzania. The study will have three phases: baseline survey, follow-up, and end-line survey. Participants will be newly diagnosed adult cancer patients who are eligible for anthracycline-based chemotherapy. A total of 427 participants will be involved. At baseline, participants will be evaluated for the cardiovascular risk factors before commencing chemotherapy. During follow-up, participants will commence their prescribed anthracycline and the cardiovascular indices monitored throughout until the patient completes the prescribed anthracycline cycles. After completing the prescribed anthracycline cycles, an end line survey will be conducted to evaluate any change in cardiovascular indices. The outcome variable in this study will be the change in biochemical data (high-density lipoprotein, low-density lipoprotein, triglyceride, and troponin I), blood pressure, and electrocardiographic information (heart rate and Bazett QT interval). Independent variables will be demographic characteristics, risk factors for cardiovascular disorders, current dietary practices, and body mass index. Descriptive statistics will be used to describe the participants. Independent and paired t-tests will be performed to make comparisons between and within groups. P-values <0.05 will be considered statistically significant. The results of this study will help clinicians and policymakers to understand the burden of early cardiovascular outcomes and plan for appropriate preventive strategies.

A guideline for the outpatient management of glycaemic control in people with cancer

Individuals with cancer are at increased risk of developing new-onset diabetes mellitus and hyperglycaemia, and an estimated 20% of people with cancer already have an underlying diagnosis of diabetes mellitus. People with both cancer and diabetes may have an increased risk of toxicities, hospital admissions and morbidity, with hyperglycaemia potentially attenuating the efficacy of chemotherapy often secondary to dose reductions and early cessation. Numerous studies have demonstrated that hyperglycaemia is prognostic of worse overall survival and risk of cancer recurrence. These guidelines aim to provide the oncology/haemato-oncology and diabetes multidisciplinary teams with the tools to manage people with diabetes commencing anti-cancer/glucocorticoid therapy, as well as identifying individuals without a known diagnosis of diabetes who are at risk of developing hyperglycaemia and new-onset diabetes.