Background: Although ethnomedically and taxonomically guided searches for new medicinal plants can improve the percentage of plants found containing active compounds when compared to random sampling, ethnobotany has fulfilled little of its promise in the last few decades to deliver a bounty of new, laboratory-proven medicinal plants and compounds. It is quite difficult to test, isolate, and elucidate the structure and mechanism of compounds from the plethora of new medicinal plant uses described each year with limited laboratory time and resources and the high cost of clinical trials of new drug candidates.
Methods: A new quantitative theoretical framework of mathematical formulas called "relational efficacy" is proposed that should narrow down this search for new plant-derived medicines based on the hypothesis that closely related plants used to treat closely related diseases in distantly related cultures have a higher probability of being effective because they are more likely to be independent discoveries of similar plant compounds and disease mechanisms. A prerequisite to this hypothesis, the idea that empirical testing in traditional medicine will lead to choosing similar medicinal plants and therefore the medicinal flora of two distant cultures will prove to be more similar than their general flora, is tested using resampling statistics on cross-cultural field data of the plants used by the Malinké of Mali and the Asháninka of Peru to treat the diseases malaria, African sleeping sickness, Chagas' disease, leishmaniasis, diabetes, eczema, asthma, and uterine fibroids.
Results: In this case, the similarity of the medicinal floras is found to be significantly greater than the similarity of the general floras, but only when the diseases in question are grouped into the categories of parasitic and autoimmune diseases.
Conclusion: If the central theoretical framework of this hypothesis is shown to be true, it will allow the synthesis of medicinal plant information from around the world to pinpoint the species with the highest potential efficacy to take into the laboratory and analyze further, ultimately saving much field and laboratory time and resources.