scholarly journals Learning from amyloid trials in Alzheimer's disease. A virtual patient analysis using a quantitative systems pharmacology approach

2020 ◽  
Vol 16 (6) ◽  
pp. 862-872 ◽  
Author(s):  
Hugo Geerts ◽  
Athan Spiros
2020 ◽  
Vol 78 (1) ◽  
pp. 413-424
Author(s):  
Hugo Geerts ◽  
Athan Spiros

Background: Many Alzheimer’s disease patients in clinical practice are on polypharmacy for treatment of comorbidities. Objective: While pharmacokinetic interactions between drugs have been relatively well established with corresponding treatment guidelines, many medications and common genotype variants also affect central brain circuits involved in cognitive trajectory, leading to complex pharmacodynamic interactions and a large variability in clinical trials. Methods: We applied a mechanism-based and ADAS-Cog calibrated Quantitative Systems Pharmacology biophysical model of neuronal circuits relevant for cognition in Alzheimer’s disease, to standard-of-care cholinergic therapy with COMTVal158Met, 5-HTTLPR rs25531, and APOE genotypes and with benzodiazepines, antidepressants, and antipsychotics, all together 9,585 combinations. Results: The model predicts a variability of up to 14 points on ADAS-Cog at baseline (COMTVV 5-HTTLPRss APOE 4/4 combination is worst) and a four-fold range for the rate of progression. The progression rate is inversely proportional to baseline ADAS-Cog. Antidepressants, benzodiazepines, first-generation more than second generation, and most antipsychotics with the exception of aripiprazole worsen the outcome when added to standard-of-care in mild cases. Low dose second-generation benzodiazepines revert the negative effects of risperidone and olanzapine, but only in mild stages. Non APOE4 carriers with a COMTMM and 5HTTLPRLL are predicted to have the best cognitive performance at baseline but deteriorate somewhat faster over time. However, this effect is significantly modulated by comedications. Conclusion: Once these simulations are validated, the platform can in principle provide optimal treatment guidance in clinical practice at an individual patient level, identify negative pharmacodynamic interactions with novel targets and address protocol amendments in clinical trials.


2012 ◽  
Vol 8 (4S_Part_5) ◽  
pp. P192-P193
Author(s):  
Timothy Nicholas ◽  
Hugh Barton ◽  
Yasong Lu ◽  
Sridhar Duvvuri ◽  
Tatiana Karelina ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Shengwei Liu ◽  
Cui He ◽  
Yuan Liao ◽  
Hailin Liu ◽  
Wanli Mao ◽  
...  

Background and Aim. Alzheimer’s disease (AD) is a common neurological disorder worldwide. In traditional Chinese medicine (TCM), Acori Tatarinowii Rhizoma (ATR) and Codonopsis Radix (CR) are common herbs used to treat AD. However, due to the many active ingredients and targets in these herbs, it is difficult to clarify the synergistic mechanism of ATR and CR. To reveal the multicomponent synergistic mechanism of ATR and CR in Alzheimer’s disease, we analyzed important components, drug targets, and crucial pathways using a systems pharmacology strategy. Materials and Methods. In this study, a systems pharmacology-based strategy was used to elucidate the synergistic mechanism of Acori Tatarinowii Rhizoma and Codonopsis Radix for the treatment of AD. This novel systems pharmacology model consisted of component information, pharmacokinetic analysis, and pharmacological data. Additionally, the related pathways were compressed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and the organ distributions were determined in the BioGPS bank. Results. Sixty-eight active ingredients with suitable pharmacokinetic profiles and biological activities were selected through ADME screening in silico. Based on 62 AD-related targets, such as APP, CHRM1, and PTGS1, systematic analysis showed that these two herbs were mainly involved in the PI3K-Akt signaling pathway, MAPK signaling pathway, neuroactive ligand-receptor interaction, and fluid shear stress and atherosclerosis, indicating that they had a synergistic effect on AD. However, ATR acted on the KDR gene, while CR acted on IGF1R, MET, IL1B, and CHUK, showing that they also had complementary effects on AD. The ingredient contribution score involved 29 ingredients contributing 90.14% of the total contribution score of this formula for AD treatment, which emphasized that the effective therapeutic effects of these herbs for AD were derived from both ATR and CR, not a single herb. Organ distribution showed that the targets of the active ingredients were mainly located in the whole blood, the brain, and the muscle, which are associated with AD. Conclusions. In sum, our findings suggest that the systems pharmacology methods successfully revealed the synergistic and complementary mechanisms of ATR and CR for the treatment of AD.


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