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From Computational Prediction to Behavioral Validation: Pharmacokinetic Profiling and Molecular Docking of Phytochemicals as Essential Stages Before Preclinical Testing in Alzheimer’s Disease Models
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1  Laboratory of Animal Physiology, Faculty of Biology, "Alexandru Ioan Cuza" University of Iasi, Bd. Carol I, No. 11, Iasi, 700506, Romania
Academic Editor: Daniela De Vita

Abstract:

Rubus fruticosus L. phytochemicals represent a rich source of bioactive molecules with antioxidant, anti-inflammatory, and neuroprotective potential. These polyphenolic compounds enhance endogenous antioxidant enzymes such as superoxide dismutase and catalase, mitigating oxidative stress and potentially delaying cognitive decline. Their ability to stimulate α-secretase and inhibit β-secretase suggests interference with amyloid aggregation, a central process in Alzheimer’s disease (AD) pathology.

Comprehensive in silico pharmacokinetic profiling (SwissADME, pkCSM, DEEP-pk) indicated high gastrointestinal absorption for all analyzed phytochemicals (p-coumaric acid: 91.12%, daidzein: 93.04%, 4-hydroxybenzoic acid: 77.49%, cinnamic acid: 94.02%, ferulic acid: 94.10%, guaiacol: 93.37%) and favorable blood–brain barrier permeability. Compounds exhibited logBB values from -0.315 to 0.205 and logPS values from -2.5 to -1.924, surpassing the respective thresholds (-1 and -3) and demonstrating superior predicted brain accessibility compared with galantamine (logPS = -3.022).

Compounds from R. fruticosus L. fractions displayed consensus LogP values < 5 (1.05-2.24), TPSA ≤ 140 Ų (29.46-70.67 Ų), and bioavailability scores of 0.85, except for daidzein and guaiacol (0.55). Unlike galantamine, these molecules are not predicted to be P-glycoprotein substrates, suggesting reduced efflux and improved CNS bioavailability. Compared with galantamine, the phytochemicals exhibited equal or higher predicted oral bioavailability (0.85) and full compliance with Lipinski, Veber, and Egan filters, with at most a single Muegge or Ghose rule violation. Predicted skin permeability values further support their systemic absorption potential.

To validate these predictions, 5xFAD mice (n = 40) were randomly assigned to three treatment groups (FT, FH, LH; n = 10 per extract, with 5 mice receiving 50 mg/kg and 5 receiving 100 mg/kg), a negative control group (NaCl, n = 5), and a positive control group treated with a standard AD medication, galantamine (n = 5). All treatments were administered via oral gavage for seven consecutive days and one hour prior to behavioral testing. Behavioral outcomes were assessed using standardized tests, and statistical analyses were performed to determine significance between groups (p < 0.05). Cognition, as well as anxiety- and depression-like behaviors, were evaluated using the Y-Maze, Open Field Test, Novel Object Recognition Test, Elevated Plus Maze, and Forced Swim Test. Behavioral assays revealed significant improvements in spatial and recognition memory, as well as anxiety- and depression-like behaviors. Distinct dose-dependent patterns were observed among the R. fruticosus L. fractions. FT exhibited an inverse dose–response, with the 50 mg/kg dose yielding superior cognitive performance, enhanced working and recognition memory, and reduced anxiety- and depression-like behaviors. In contrast, FH showed a classical dose–response, with higher doses (100 mg/kg) improving cognition, while lower doses increased general locomotor activity without enhancing cognition, likely reflecting general arousal rather than targeted exploration. These findings corroborate the computational predictions of BBB permeability and multitarget engagement, highlighting the translational relevance of this integrated approach.

This combined computational-experimental strategy highlights R. fruticosus L. phytochemicals as promising multitarget candidates for AD therapy and underscores the value of in silico modeling in prioritizing molecules with optimal CNS accessibility for preclinical investigation.

Keywords: Alzheimer’s Disease; Phytochemicals; Pharmacokinetic Profiling; Molecular Docking; Behavioral testing;
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