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Thapa R, Ahmad Bhat A, Shahwan M, Ali H, PadmaPriya G, Bansal P, Rajotiya S, Barwal A, Siva Prasad GV, Pramanik A, Khan A, Hing Goh B, Dureja H, Kumar Singh S, Dua K, Gupta G. Proteostasis disruption and senescence in Alzheimer's disease pathways to neurodegeneration. Brain Res 2024; 1845:149202. [PMID: 39216694 DOI: 10.1016/j.brainres.2024.149202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 07/29/2024] [Accepted: 08/25/2024] [Indexed: 09/04/2024]
Abstract
Alzheimer's Disease (AD) is a progressive neurological disease associated with behavioral abnormalities, memory loss, and cognitive impairment that cause major causes of dementia in the elderly. The pathogenetic processes cause complex effects on brain function and AD progression. The proper protein homeostasis, or proteostasis, is critical for cell health. AD causes the buildup of misfolded proteins, particularly tau and amyloid-beta, to break down proteostasis, such aggregates are toxic to neurons and play a critical role in AD pathogenesis. The rise of cellular senescence is accompanied by aging, marked by irreversible cell cycle arrest and the release of pro-inflammatory proteins. Senescent cell build-up in the brains of AD patients exacerbates neuroinflammation and neuronal degeneration. These cells senescence-associated secretory phenotype (SASP) also disturbs the brain environment. When proteostasis failure and cellular senescence coalesce, a cycle is generated that compounds each other. While senescent cells contribute to proteostasis breakdown through inflammatory and degradative processes, misfolded proteins induce cellular stress and senescence. The principal aspects of the neurodegenerative processes in AD are the interaction of cellular senescence and proteostasis failure. This review explores the interconnected roles of proteostasis disruption and cellular senescence in the pathways leading to neurodegeneration in AD.
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Affiliation(s)
- Riya Thapa
- Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, India
| | - Asif Ahmad Bhat
- Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, India
| | - Moyad Shahwan
- Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, UAE
| | - Haider Ali
- Centre for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, India; Department of Pharmacology, Kyrgyz State Medical College, Bishkek, Kyrgyzstan
| | - G PadmaPriya
- Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India
| | - Pooja Bansal
- Department of Allied Healthcare and Sciences, Vivekananda Global University, Jaipur, Rajasthan-303012, India
| | - Sumit Rajotiya
- NIMS Institute of Pharmacy, NIMS University Rajasthan, Jaipur, India
| | - Amit Barwal
- Chandigarh Pharmacy College, Chandigarh Group of College, Jhanjeri, Mohali - 140307, Punjab, India
| | - G V Siva Prasad
- Department of Chemistry, Raghu Engineering College, Visakhapatnam, Andhra Pradesh-531162, India
| | - Atreyi Pramanik
- School of Applied and Life Sciences, Division of Research and Innovation, Uttaranchal University, Dehradun, India
| | - Abida Khan
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Northern Border University, Rafha 91911, Saudi Arabia
| | - Bey Hing Goh
- Sunway Biofunctional Molecules Discovery Centre (SBMDC), School of Medical and Life Sciences, Sunway University, Sunway, Malaysia; Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW, Australia; Biofunctional Molecule Exploratory Research Group (BMEX), School of Pharmacy, Monash University Malaysia, Bandar Sunway, Selangor Darul Ehsan, 47500, Malaysia
| | - Harish Dureja
- Department of Pharmaceutical Sciences, Maharshi Dayanand University, Rohtak, 124001, India
| | - Sachin Kumar Singh
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab 144411, India; Faculty of Health, Australian Research Center in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Kamal Dua
- Faculty of Health, Australian Research Center in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia; Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Gaurav Gupta
- Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, UAE; Centre for Research Impact & Outcome, Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab 140401, India.
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Wu S, Chang HY, Chowdhury EA, Huang HW, Shah DK. Investigation of Antibody Pharmacokinetics in the Brain Following Intra-CNS Administration and Development of PBPK Model to Characterize the Data. AAPS J 2024; 26:29. [PMID: 38443635 DOI: 10.1208/s12248-024-00898-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 02/12/2024] [Indexed: 03/07/2024] Open
Abstract
Despite the promising potential of direct central nervous system (CNS) antibody administration to enhance brain exposure, there remains a significant gap in understanding the disposition of antibodies following different intra-CNS injection routes. To bridge this knowledge gap, this study quantitatively investigated the brain pharmacokinetics (PK) of antibodies following intra-CNS administration. The microdialysis samples from the striatum (ST), cerebrospinal fluid (CSF) samples through cisterna magna (CM) puncture, plasma, and brain homogenate samples were collected to characterize the pharmacokinetics (PK) profiles of a non-targeting antibody, trastuzumab, following intracerebroventricular (ICV), intracisternal (ICM), and intrastriatal (IST) administration. For a comprehensive analysis, these intra-CNS injection datasets were juxtaposed against our previously acquired intravenous (IV) injection data obtained under analogous experimental conditions. Our findings highlighted that direct CSF injections, either through ICV or ICM, resulted in ~ 5-6-fold higher interstitial fluid (ISF) drug exposure than IV administration. Additionally, the low bioavailability observed following IST administration indicates the existence of a local degradation process for antibody elimination in the brain ISF along with the ISF bulk flow. The study further refined a physiologically based pharmacokinetic (PBPK) model based on new observations by adding the perivascular compartments, oscillated CSF flow, and the nonspecific uptake and degradation of antibodies by brain parenchymal cells. The updated model can well characterize the antibody PK following systemic and intra-CNS administration. Thus, our research offers quantitative insight into antibody brain disposition pathways and paves the way for determining optimal dosing and administration strategies for antibodies targeting CNS disorders.
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Affiliation(s)
- Shengjia Wu
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, New York, USA
| | - Hsueh-Yuan Chang
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, New York, USA
| | - Ekram Ahmed Chowdhury
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, New York, USA
| | - Hsien Wei Huang
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, New York, USA
| | - Dhaval K Shah
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, New York, USA.
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Liu X, Wang W, Chen J, Chen D, Tao Y, Ouyang D. PBPK/PD Modeling of Nifedipine for Precision Medicine in Pregnant Women: Enhancing Clinical Decision-Making for Optimal Drug Therapy. Pharm Res 2024; 41:63-75. [PMID: 38049651 DOI: 10.1007/s11095-023-03638-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/24/2023] [Indexed: 12/06/2023]
Abstract
OBJECTIVE This study aims to develop physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) predictive models for nifedipine in pregnant women, enhancing precision medicine and reducing adverse reactions for both mothers and infants. METHODS A PBPK/PD model was constructed using PK-Sim, MoBi, and MATLAB software, integrating literature and pregnancy-specific physiological information. The process involved: (1) establishing and validating a PBPK model for serum clearance after intravenous administration in non-pregnant individuals, (2) establishing and validating a PBPK model for serum clearance after oral administration in non-pregnant individuals, (3) constructing and validating a PBPK model for enzyme clearance after oral administration in non-pregnant individuals, and (4) adjusting the PBPK model structure and enzyme parameters according to pregnant women and validating it in oral administration. (5) PK/PD model was explored through MATLAB, and the PBPK and PK/PD models were integrated to form the PBPK/PD model. RESULTS The Nifedipine PBPK model's predictive accuracy was confirmed by non-pregnant and pregnant validation studies. The developed PBPK/PD model accurately predicted maximum antihypertensive effects for clinical doses of 5, 10, and 20 mg. The model suggested peak effect at 0.86 h post-administration, achieving blood pressure reductions of 5.4 mmHg, 14.3 mmHg, and 21.3 mmHg, respectively. This model provides guidance for tailored dosing in pregnancy-induced hypertension based on targeted blood pressure reduction. CONCLUSION Based on available literature data, the PBPK/PD model of Nifedipine in pregnancy demonstrated good predictive performance. It will help optimize individualized dosing of Nifedipine, improve treatment outcomes, and minimize the risk of adverse reactions in mothers and infants.
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Affiliation(s)
- Xinyang Liu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS)/FHS, University of Macau, Avenida da Universidade, Taipa, Macau, China
- Department of Pharmacy, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China
| | - Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS)/FHS, University of Macau, Avenida da Universidade, Taipa, Macau, China
| | - Jingsi Chen
- Department of Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Key Laboratories for Major Obstetric Diseases of Guangdong Province, Guangzhou, 510150, China
| | - Dunjin Chen
- Department of Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Key Laboratories for Major Obstetric Diseases of Guangdong Province, Guangzhou, 510150, China
| | - Yong Tao
- Department of Ophthalmology, Beijing Chaoyang Hospital, Capital Medical University, No. 8, South Road of Worker's Stadium, Chaoyang District, Beijing, 100020, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS)/FHS, University of Macau, Avenida da Universidade, Taipa, Macau, China.
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences (FHS), University of Macau, Macau, China.
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Geerts H, Bergeler S, Walker M, van der Graaf PH, Courade JP. Analysis of clinical failure of anti-tau and anti-synuclein antibodies in neurodegeneration using a quantitative systems pharmacology model. Sci Rep 2023; 13:14342. [PMID: 37658103 PMCID: PMC10474108 DOI: 10.1038/s41598-023-41382-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 08/25/2023] [Indexed: 09/03/2023] Open
Abstract
Misfolded proteins in Alzheimer's disease and Parkinson's disease follow a well-defined connectomics-based spatial progression. Several anti-tau and anti-alpha synuclein (aSyn) antibodies have failed to provide clinical benefit in clinical trials despite substantial target engagement in the experimentally accessible cerebrospinal fluid (CSF). The proposed mechanism of action is reducing neuronal uptake of oligomeric protein from the synaptic cleft. We built a quantitative systems pharmacology (QSP) model to quantitatively simulate intrasynaptic secretion, diffusion and antibody capture in the synaptic cleft, postsynaptic membrane binding and internalization of monomeric and oligomeric tau and aSyn proteins. Integration with a physiologically based pharmacokinetic (PBPK) model allowed us to simulate clinical trials of anti-tau antibodies gosuranemab, tilavonemab, semorinemab, and anti-aSyn antibodies cinpanemab and prasineuzumab. Maximal target engagement for monomeric tau was simulated as 45% (semorinemab) to 99% (gosuranemab) in CSF, 30% to 99% in ISF but only 1% to 3% in the synaptic cleft, leading to a reduction of less than 1% in uptake of oligomeric tau. Simulations for prasineuzumab and cinpanemab suggest target engagement of free monomeric aSyn of only 6-8% in CSF, 4-6% and 1-2% in the ISF and synaptic cleft, while maximal target engagement of aggregated aSyn was predicted to reach 99% and 80% in the synaptic cleft with similar effects on neuronal uptake. The study generates optimal values of selectivity, sensitivity and PK profiles for antibodies. The study identifies a gradient of decreasing target engagement from CSF to the synaptic cleft as a key driver of efficacy, quantitatively identifies various improvements for drug design and emphasizes the need for QSP modelling to support the development of tau and aSyn antibodies.
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Affiliation(s)
- Hugo Geerts
- Certara US, 100 Overlook Centre, Suite 101, Princeton, NJ, 08540, USA.
| | - Silke Bergeler
- Certara US, 100 Overlook Centre, Suite 101, Princeton, NJ, 08540, USA
- Bristol-Meyers-Squibb, Lawrenceville, NJ, 08648, USA
| | - Mike Walker
- Certara UK, Canterbury Innovation Centre, University Road, Canterbury, CT2 7FG, Kent, UK
| | - Piet H van der Graaf
- Certara UK, Canterbury Innovation Centre, University Road, Canterbury, CT2 7FG, Kent, UK
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Geerts H, Bergeler S, Lytton WW, van der Graaf PH. Computational neurosciences and quantitative systems pharmacology: a powerful combination for supporting drug development in neurodegenerative diseases. J Pharmacokinet Pharmacodyn 2023:10.1007/s10928-023-09876-6. [PMID: 37505397 DOI: 10.1007/s10928-023-09876-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023]
Abstract
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.
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Affiliation(s)
| | | | - William W Lytton
- Downstate Health Science University, State University of New York, Brooklyn, USA
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Li R, Craig M, D'Argenio DZ, Betts A, Mager DE, Maurer TS. Editorial: Model-informed decision making in the preclinical stages of pharmaceutical research and development. Front Pharmacol 2023; 14:1184914. [PMID: 37124233 PMCID: PMC10131111 DOI: 10.3389/fphar.2023.1184914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 03/27/2023] [Indexed: 05/02/2023] Open
Affiliation(s)
- Rui Li
- Translational Modeling and Simulation, Medicine Design, Pfizer Inc., Cambridge, MA, United States
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, QC, Canada
| | - David Z. D'Argenio
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Alison Betts
- Applied BioMath, LLC, Concord, MA, United States
| | - Donald E. Mager
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
- Enhanced Pharmacodynamics, LLC, Buffalo, NY, United States
| | - Tristan S. Maurer
- Translational Modeling and Simulation, Medicine Design, Pfizer Inc., Cambridge, MA, United States
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Skolariki K, Exarchos TP, Vlamos P. Computational Models for Biomarker Discovery. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1424:289-295. [PMID: 37486506 DOI: 10.1007/978-3-031-31982-2_33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disorder characterized by progressive cognitive decline. Early diagnosis and accurate prediction of disease progression are critical for developing effective therapeutic interventions. In recent years, computational models have emerged as powerful tools for biomarker discovery and disease prediction in Alzheimer's and other neurodegenerative diseases. This paper explores the use of computational models, particularly machine learning techniques, in analyzing large volumes of data and identifying patterns related to disease progression. The significance of early diagnosis, the challenges in classifying patients at the mild cognitive impairment (MCI) stage, and the potential of computational models to improve diagnostic accuracy are examined. Furthermore, the importance of incorporating diverse biomarkers, including genetic, molecular, and neuroimaging indicators, to enhance the predictive capabilities of these models is highlighted. The paper also presents case studies on the application of computational models in simulating disease progression, analyzing neurodegenerative cascades, and predicting the future development of Alzheimer's. Overall, computational models for biomarker discovery offer promising opportunities to advance our understanding of Alzheimer's disease, facilitate early diagnosis, and guide the development of targeted therapeutic strategies.
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