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Simple Summary: Though chemotherapy is generally known to be effective in the fight against cancer, its application is associated with a number of side effects, including toxic impacts on the ovaries. The most ovotoxic cytostatic chemotherapeutics are the classical alkylating compounds, particularly cyclophosphamide. Thus, it is of utmost importance to find effective means to protect ovaries against the negative influence of chemotherapeutic agents. This review paper presents the results of the current research work on the hitherto proposed agents potentially protecting the functions and state of ovaries exposed to chemotherapy. A large body of promising results have been reported, but as presented, it is necessary to undertake thorough and comprehensive studies aimed at providing an explicit evaluation of the efficacy of selected ovoprotecting agents and their possible clinical use in the future. Chemotherapy is one of the leading cancer treatments. Unfortunately, its use can contribute to several side effects, including gynotoxic effects in women. Ovarian reserve suppression and estrogen deficiency result in reduced quality of life for cancer patients and are frequently the cause of infertility and early menopause. Classic alkylating cytostatics are among the most toxic chemotherapeutics in this regard. They cause DNA damage in ovarian follicles and the cells they contain, and they can also induce oxidative stress or affect numerous signaling pathways. In vitro tests, animal models, and a few studies among women have investigated the effects of various agents on the protection of the ovarian reserve during classic chemotherapy. In this review article, we focused on the possible beneficial effects of selected hormones (anti-Müllerian hormone, ghrelin, luteinizing hormone, melatonin), agents affecting the activity of apoptotic pathways and modulating gene expression (C1P, S1P, microRNA), and several natural (quercetin, rapamycin, resveratrol) and synthetic compounds (bortezomib, dexrazoxane, goserelin, gonadoliberin analogs, imatinib, metformin, tamoxifen) in preventing gynotoxic effects induced by commonly used cytostatics. The presented line of research appears to provide a promising strategy for protecting and/or improving the ovarian reserve in the studied group of cancer patients. However, well-designed clinical trials are needed to unequivocally assess the effects of these agents on improving hormonal function and fertility in women treated with ovotoxic anticancer drugs. [ABSTRACT FROM AUTHOR]

In the contemporary era, the exploration of machine learning (ML) has gained widespread attention and is being leveraged to augment traditional methodologies in quantitative structure–activity relationship (QSAR) investigations. The principal objective of this research was to assess the anticancer potential of colchicine-based compounds across five distinct cell lines. This research endeavor ultimately sought to construct ML models proficient in forecasting anticancer activity as quantified by the I C 50 value, while concurrently generating innovative colchicine-derived compounds. The resistance index (RI) is computed to evaluate the drug resistance exhibited by LoVo/DX cells relative to LoVo cancer cell lines. Meanwhile, the selectivity index (SI) is computed to determine the potential of a compound to demonstrate superior efficacy against tumor cells compared to its toxicity against normal cells, such as BALB/3T3. We introduce a novel ML system adept at recommending novel chemical structures predicated on known anticancer activity. Our investigation entailed the assessment of inhibitory capabilities across five cell lines, employing predictive models utilizing various algorithms, including random forest, decision tree, support vector machines, k-nearest neighbors, and multiple linear regression. The most proficient model, as determined by quality metrics, was employed to predict the anticancer activity of novel colchicine-based compounds. This methodological approach yielded the establishment of a library encompassing new colchicine-based compounds, each assigned an I C 50 value. Additionally, this study resulted in the development of a validated predictive model, capable of reasonably estimating I C 50 values based on molecular structure input. [ABSTRACT FROM AUTHOR]