Synergy of botanical drug extracts from Dracaena cochinchinensis stemwood and Ardisia elliptica fruit in multifunctional effects on neuroprotection and anti-inflammation

Front Pharmacol. 2024 May 1:15:1399549. doi: 10.3389/fphar.2024.1399549. eCollection 2024.

Abstract

Combination therapy is one of the promising approaches in developing therapeutics to cure complex diseases, such as Alzheimer's disease (AD). In Thai traditional medicines, the clinical application often comprises multiple botanical drugs as a formulation. The synergistic interactions between botanical drugs in combination therapies are proposed to have several advantages, including increased therapeutic efficacy, and decreased toxicity and/or adverse effects. This study aimed to explore the therapeutic functions of a botanical hybrid preparation (BHP) of two botanical drugs within a traditional multi-herbal formulation. The synergistic actions of BHP of Dracaena cochinchinensis stemwood (DCS) and Ardisia elliptica fruit (AEF) at a specific ratio of 1:9 w/w were illustrated in neuroprotection and anti-inflammation. In cultured PC12 cells, BHP of DCS and AEF showed synergistic functions in inducing neuronal differentiation, characterized by neurofilament expression and neurite outgrowth. In addition, BHP of DCS and AEF exhibited a synergistic effect in inhibiting the aggregation of Aβ, a hallmark of AD pathology. The activated BV2 microglial cells induced by LPS were synergistically suppressed by the BHP of DCS and AEF, as evaluated by the expression of pro-inflammatory markers, including TNF-α, IL-1β, and iNOS, as well as the morphological change of microglial cells. The findings suggested that the effects of BHP of DCS and AEF were greater than individual botanical drugs in a specific ratio of 1:9 w/w to enhance neuroprotective and anti-inflammatory functions.

Keywords: Alzheimer’s disease; Ardisia elliptica; Dracaena cochinchinensis; amyloid beta; anti-inflammation; botanical drug combination; neuronal differentiation.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work is supported by Hong Kong Research Grant Council Hong Kong (GRF 16100921); Zhongshan Municipal Bureau of Science and Technology (2019AG035); Guangzhou Science and Technology Committee Research Grants (GZSTI16SC02 and GZSTI17SC02); GBA Institute of Collaborate Innovation (GICI-022); The Key-Area Research and Development Program of Guangdong Province (2020B1111110006); Special project of Foshan University of Science and Technology in 2019 (FSUST19-SRI10); Hong Kong RGC Theme-based Research Scheme (T13-605/18-W); Hong Kong Innovation Technology Fund (ITS/500/18FP; MHP/004/21; GHP/016/21SZ; ITCPD/17-9; ITC-CNERC14SC01); TUYF19SC02, PD18SC01 and HMRF18SC06; HMRF20SC07, AFD20SC01; Shenzhen Science and Technology Innovation Committee (ZDSYS201707281432317).