Alterations in mitochondrial structure and functions have long been observed in cancer cells. Targeting mitochondria as a cancer therapeutic strategy has gained momentum in the recent years. The signaling pathways that govern mitochondrial function, apoptosis and molecules that affect mitochondrial integrity and cell viability have been important topics of the recent review in the literature. In this article, we first briefly summarize the rationale and biological basis for developing mitochondrial-targeted compounds as potential anticancer agents, and then provide key examples of small molecules that either directly impact mitochondria or functionally affect the metabolic alterations in cancer cells with mitochondrial dysfunction. The main focus is on the small molecular weight compounds with potential applications in cancer treatment. We also summarize information on the drug developmental stages of the key mitochondria-targeted compounds and their clinical trial status. The advantages and potential shortcomings of targeting the mitochondria for cancer treatment are also discussed.

Amyotrophic lateral sclerosis (ALS) is a progressive, paralytic disorder caused by selective degeneration of motor neurons in the brain and spinal cord. Our previous studies indicated that abnormal protein aggregation and dysfunctional autophagic flux might contribute to the disease pathogenesis. In this study, we have detected the role of the Ca2+ dependent autophagic pathway in ALS by using the L-type channel Ca2+ blocker, verapamil. We have found that verapamil significantly delayed disease onset, prolonged the lifespan and extended disease duration in SOD1G93A mice. Furthermore, verapamil administration rescued motor neuron survival and ameliorated skeletal muscle denervation in SOD1G93A mice. More interestingly, verapamil significantly reduced SOD1 aggregation and improved autophagic flux, which might be mediated the inhibition of calpain 1 activation in the spinal cord of SOD1G93A mice. Furthermore, we have demonstrated that verapamil reduced endoplasmic reticulum stress and suppressed glia activation in SOD1G93A mice. Collectively, our study indicated that verapamil is neuroprotective in the ALS mouse model and the Ca2+-dependent autophagic pathway is a possible therapeutic target for the treatment of ALS.

Background: Personalized prediction of the risk of symptomatic intracerebral hemorrhage (sICH) after stroke thrombolysis is clinically useful. Machine-learning-based modeling may provide the personalized prediction of the risk of sICH after stroke thrombolysis. Methods: We identified 2578 thrombolysis-treated ischemic stroke patients between January 2013 and December 2016 from a multicenter database, where 70% were used to train models and the remaining 30% were used as the nominal test sets. Another 136 consecutive tissue plasminogen-activated-treated patients between January 2017 and December 2017 from our institute were enrolled as the independent test sets for clinical usability evaluation. Five machine-learning models were developed to predict the risk of sICH after stroke thrombolysis, and the receiving operating characteristic (ROC) was used to compare the prediction performance. Results: In total, 2237 cases were included in our study, of which 102 had sICH transformation (4.56%). Finally, the three-layer neuro network was selected with the best performance on nominal test sets (AUC = 0.82). The probability of the model score was further categorized into three risk ranks (18.97%, 5.63%, and 0.81%) according to the risk distribution. Implementing our system in clinical practice was associated with reduced computed tomography (CT)-to-treatment time (CTT; 41 min versus 52 min, p < 0.001). All sICH patients were correctly predicted to be within the high-sICH risk rank. Conclusions: The machine-learning-based modeling is feasible for providing personalized risk prediction of sICH after stroke thrombolysis, and is able to reduce the CTT. More data are needed to further optimize the model and improve the accuracy of prediction.