Key result
This review evaluates different rodent screening models for experimentally induced polycystic ovarian disease, highlighting their parallels to human PCOD and associated diagnostic parameters.
Why the study?
Effective treatment for a complete cure of PCOD remains challenging, and developing a fully convincing animal model to evaluate new strategies is complicated because rodents are multi-ovular species.
This review summarizes the current rodent models used for screening pharmacological treatments for polycystic ovarian disease and the key diagnostic parameters evaluated in these models.
May guide preclinical PCOD drug screening; leaves open clinical translation pending validation.
PCOD is a complex heterogenous metabolic disorder affecting 10 % of women at their reproductive age. A well effective treatment for a complete cure for PCOD is still a challenge and evaluation of new strategies to treat this disease is of great priority. This review focuses on two aspects of PCOD, namely types of rodent screening models and diagnosis of major parameters. A fully convincing animal model to study PCOD is still a challenge as they are multi-ovular species. But in many ways, the rodent models are similar to human PCOD. This study explores on the parallels and problems associated with the use of different rodent models. Oligo- or anovulation, clinical or biochemical hyperandrogenism and polycystic ovaries constitute the key clinical features on which the diagnosis should be based. Determination of diagnosis parameters such as estrus cyclicity, insulin sensitivity, lipid profile, anti-oxidant status, weight of reproductive system and histopathological features of ovary are discussed along with the currently available treatment methods for PCOD.
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Selvaraj et al. (2021) conducted a review in Polycystic Ovarian Disease (PCOD). Rodent screening models was evaluated. This review evaluates different rodent screening models for experimentally induced polycystic ovarian disease, highlighting their parallels to human PCOD and associated diagnostic parameters.
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