Simulating the Effects of Common Comedications and Genotypes on Alzheimer's Cognitive Trajectory Using a Quantitative Systems Pharmacology Approach

Efficacy of selective serotonin reuptake inhibitors-related antidepressants in Alzheimer's disease: a meta-analysis

OBJECTIVE

To study the effects of selective serotonin reuptake inhibitors (SSRIs) on cognitive functions, mental improvements, and adverse effects in patients with Alzheimer's disease (AD).

METHODS

Registered in INPLASY (INPLASY202450004), five drugs (citalopram, s-citalopram, quetiapine, olanzapine, and sertraline) were selected as representatives. A comprehensive search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library up to May 15, 2024. Search terms were combined using Boolean operators, specifically 'AND' between different categories (e.g., 'Alzheimer's Disease' AND 'SSRIs') and 'OR' within the same category (e.g., 'citalopram OR s-citalopram OR quetiapine OR olanzapine OR sertraline'), to ensure a thorough retrieval of relevant studies. The selection followed rigorous inclusion and exclusion criteria for meta-analysis.

RESULTS

Fourteen articles from 1118 were selected for meta-analysis. The indicators, including Neuropsychiatric Inventory (NPI), Mini-Mental State Examination (MMSE), Brief Psychiatric Rating Scale (BPRS), and Cornell Scale for Depression in Dementia (CSDD), were used to assess the effects of the drugs on AD treatment. According to the results of NPI, CSDD, BPRS, MMSE, and security assessments, the five antidepressants have significant advantages in AD treatment compared with placebo, while the MMSE of the patient treated with the antidepressants did not show notable changes compared with patients treated only with placebo. Statistical analyses were conducted using Review Manager 5.3, employing random-effects models to account for study heterogeneity and sensitivity analyses to test the robustness of our findings.

CONCLUSION

This study suggests that SSRI-related antidepressants have great potential values in AD treatment, and further research on the application of SSRI-related antidepressants in AD treatment is necessary.

Computational neurosciences and quantitative systems pharmacology: a powerful combination for supporting drug development in neurodegenerative diseases

Successful clinical development of new therapeutic interventions is notoriously difficult, especially in neurodegenerative diseases, where predictive biomarkers are scarce and functional improvement is often based on patient's perception, captured by structured interviews. As a consequence, mechanistic modeling of the processes relevant to therapeutic interventions in CNS disorders has been lagging behind other disease indications, probably because of the perceived complexity of the brain. However in this report, we develop the argument that a combination of Computational Neurosciences and Quantitative Systems Pharmacology (QSP) modeling of molecular pathways is a powerful simulation tool to enhance the probability of successful drug development for neurodegenerative diseases. Computational Neurosciences aims to predict action potential dynamics and neuronal circuit activation that are ultimately linked to behavioral changes and clinically relevant functional outcomes. These processes can not only be affected by the disease state, but also by common genotype variants on neurotransmitter-related proteins and the psycho-active medications often prescribed in these patient populations. Quantitative Systems Pharmacology (QSP) modeling of molecular pathways allows to simulate key pathological drivers of dementia, such as protein aggregation and neuroinflammatory responses. They often impact neurotransmitter homeostasis and voltage-gated ion-channels or lead to mitochondrial dysfunction, ultimately leading to changes in action potential dynamics and clinical readouts. Combining these two modeling approaches can lead to better actionable understanding of the many non-linear pharmacodynamic processes active in the human diseased brain. Practical applications include a rational selection of the optimal doses in combination therapies, identification of subjects more likely to respond to treatment, a more balanced stratification of treatment arms in terms of comedications, disease status and common genotype variants and re-analysis of small clinical trials to uncover a possible clinical signal. Ultimately this will lead to a higher success rate of bringing new therapeutics to the right patient populations.

Modeling Clinical Phenotype Variability: Consideration of Genomic Variations, Computational Methods, and Quantitative Proteomics

Advances in biomedical and computer technologies have presented the modeling community the opportunity for mechanistically modeling and simulating the variability in a disease phenotype or in a drug response. The capability to quantify response variability can inform a drug development program. Quantitative systems pharmacology scientists have published various computational approaches for creating virtual patient populations (VPops) to model and simulate drug response variability. Genomic variations can impact disease characteristics and drug exposure and response. Quantitative proteomics technologies are increasingly used to facilitate drug discovery and development and inform patient care. Incorporating variations in genomics and quantitative proteomics may potentially inform creation of VPops to model and simulate virtual patient trials, and may help account for, in a predictive manner, phenotypic variations observed clinically.

Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease

With the approval of aducanumab on the “Accelerated Approval Pathway” and the recognition of amyloid load as a surrogate marker, new successful therapeutic approaches will be driven by combination therapy as was the case in oncology after the launch of immune checkpoint inhibitors. However, the sheer number of therapeutic combinations substantially complicates the search for optimal combinations. Data-driven approaches based on large databases or electronic health records can identify optimal combinations and often using artificial intelligence or machine learning to crunch through many possible combinations but are limited to the pharmacology of existing marketed drugs and are highly dependent upon the quality of the training sets. Knowledge-driven in silico modeling approaches use multi-scale biophysically realistic models of neuroanatomy, physiology, and pathology and can be personalized with individual patient comedications, disease state, and genotypes to create ‘virtual twin patients’. Such models simulate effects on action potential dynamics of anatomically informed neuronal circuits driving functional clinical readouts. Informed by data-driven approaches this knowledge-driven modeling could systematically and quantitatively simulate all possible target combinations for a maximal synergistic effect on a clinically relevant functional outcome. This approach seamlessly integrates pharmacokinetic modeling of different therapeutic modalities. A crucial requirement to constrain the parameters is the access to preferably anonymized individual patient data from completed clinical trials with various selective compounds. We believe that the combination of data- and knowledge driven modeling could be a game changer to find a cure for this devastating disease that affects the most complex organ of the universe.

Recent applications of quantitative systems pharmacology and machine learning models across diseases

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.