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Costa B, Gouveia MJ, Vale N. Oxidative Stress Induced by Antivirals: Implications for Adverse Outcomes During Pregnancy and in Newborns. Antioxidants (Basel) 2024; 13:1518. [PMID: 39765846 PMCID: PMC11727424 DOI: 10.3390/antiox13121518] [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: 09/27/2024] [Revised: 12/06/2024] [Accepted: 12/09/2024] [Indexed: 01/15/2025] Open
Abstract
Oxidative stress plays a critical role in various physiological and pathological processes, particularly during pregnancy, where it can significantly affect maternal and fetal health. In the context of viral infections, such as those caused by Human Immunodeficiency Virus (HIV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), oxidative stress may exacerbate complications by disrupting cellular function and immune responses. Antiviral drugs, while essential in managing these infections, can also contribute to oxidative stress, potentially impacting both the mother and the developing fetus. Understanding the mechanisms by which antivirals can contribute to oxidative stress and examination of pharmacokinetic changes during pregnancy that influence drug metabolism is essential. Some research indicates that antiretroviral drugs can induce oxidative stress and mitochondrial dysfunction during pregnancy, while other studies suggest that their use is generally safe. Therefore, concerns about long-term health effects persist. This review delves into the complex interplay between oxidative stress, antioxidant defenses, and antiviral therapies, focusing on strategies to mitigate potential oxidative damage. By addressing gaps in our understanding, we highlight the importance of balancing antiviral efficacy with the risks of oxidative stress. Moreover, we advocate for further research to develop safer, more effective therapeutic approaches during pregnancy. Understanding these dynamics is essential for optimizing health outcomes for both mother and fetus in the context of viral infections during pregnancy.
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Affiliation(s)
- Bárbara Costa
- PerMed Research Group, Center for Health Technology and Services Research (CINTESIS), 4200-450 Porto, Portugal;
- CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
- Department of Community Medicine, Health Information and Decision (MEDCIDS), Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
- Centre for Parasite Biology and Immunology, Department of Infectious Diseases, National Health Institute Dr. Ricardo Jorge, 4000-055 Porto, Portugal;
| | - Maria João Gouveia
- Centre for Parasite Biology and Immunology, Department of Infectious Diseases, National Health Institute Dr. Ricardo Jorge, 4000-055 Porto, Portugal;
- Center for the Study in Animal Science (CECA/ICETA), University of Porto, 4051-401 Porto, Portugal
| | - Nuno Vale
- PerMed Research Group, Center for Health Technology and Services Research (CINTESIS), 4200-450 Porto, Portugal;
- CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
- Department of Community Medicine, Health Information and Decision (MEDCIDS), Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
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Braakman S, Pathmanathan P, Moore H. Evaluation framework for systems models. CPT Pharmacometrics Syst Pharmacol 2021; 11:264-289. [PMID: 34921743 PMCID: PMC8923730 DOI: 10.1002/psp4.12755] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 12/16/2022] Open
Abstract
As decisions in drug development increasingly rely on predictions from mechanistic systems models, assessing the predictive capability of such models is becoming more important. Several frameworks for the development of quantitative systems pharmacology (QSP) models have been proposed. In this paper, we add to this body of work with a framework that focuses on the appropriate use of qualitative and quantitative model evaluation methods. We provide details and references for those wishing to apply these methods, which include sensitivity and identifiability analyses, as well as concepts such as validation and uncertainty quantification. Many of these methods have been used successfully in other fields, but are not as common in QSP modeling. We illustrate how to apply these methods to evaluate QSP models, and propose methods to use in two case studies. We also share examples of misleading results when inappropriate analyses are used.
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Affiliation(s)
- Sietse Braakman
- Application Engineering, MathWorks Inc, Natick, Massachusetts, USA
| | - Pras Pathmanathan
- Office of Science and Engineering Laboratories (OSEL), Center for Devices and Radiological Health (CDRH), US Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Helen Moore
- Laboratory for Systems Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Florida, Gainesville, Florida, USA
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Bai JPF, Schmidt BJ, Gadkar KG, Damian V, Earp JC, Friedrich C, van der Graaf PH, Madabushi R, Musante CJ, Naik K, Rogge M, Zhu H. FDA-Industry Scientific Exchange on assessing quantitative systems pharmacology models in clinical drug development: a meeting report, summary of challenges/gaps, and future perspective. AAPS JOURNAL 2021; 23:60. [PMID: 33931790 DOI: 10.1208/s12248-021-00585-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 03/23/2021] [Indexed: 02/07/2023]
Abstract
The pharmaceutical industry is actively applying quantitative systems pharmacology (QSP) to make internal decisions and guide drug development. To facilitate the eventual development of a common framework for assessing the credibility of QSP models for clinical drug development, scientists from US Food and Drug Administration and the pharmaceutical industry organized a full-day virtual Scientific Exchange on July 1, 2020. An assessment form was used to ensure consistency in the evaluation process. Among the cases presented, QSP was applied to various therapeutic areas. Applications mostly focused on phase 2 dose selection. Model transparency, including details on expert knowledge and data used for model development, was identified as a major factor for robust model assessment. The case studies demonstrated some commonalities in the workflow of QSP model development, calibration, and validation but differ in the size, scope, and complexity of QSP models, in the acceptance criteria for model calibration and validation, and in the algorithms/approaches used for creating virtual patient populations. Though efforts are being made to build the credibility of QSP models and the confidence is increasing in applying QSP for internal decisions at the clinical stages of drug development, there are still many challenges facing QSP application to late stage drug development. The QSP community needs a strategic plan that includes the ability and flexibility to Adapt, to establish Common expectations for model Credibility needed to inform drug Labeling and patient care, and to AIM to achieve the goal (ACCLAIM).
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Affiliation(s)
- Jane P F Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20903, USA.
| | - Brian J Schmidt
- Quantitative Clinical Pharmacology, Bristol Myers Squibb, Princeton, New Jersey, USA.
| | - Kapil G Gadkar
- Development Sciences, Genentech Inc., South San Francisco, California, 94080, USA. .,Denali Therapeutics, San Francisco, California, USA.
| | - Valeriu Damian
- GSK R&D - Upper Providence, 1250 S Collegeville Rd, Collegeville, Pennsylvania, 19426, USA
| | - Justin C Earp
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20903, USA
| | | | - Piet H van der Graaf
- Certara, Canterbury, CT2 7FG, UK.,Leiden Academic Centre for Drug Research, Leiden, 2333, CC, the Netherlands
| | - Rajanikanth Madabushi
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20903, USA
| | - Cynthia J Musante
- Early Clinical Development, Pfizer Worldwide Research, Development, & Medical, 1 Portland Street, Cambridge, Massachusetts, 02139, USA
| | - Kunal Naik
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20903, USA
| | - Mark Rogge
- Quantitative Translational Science, Takeda Pharmaceuticals International Co, 40 Landsdowne Street, Cambridge, Massachusetts, 02139, USA
| | - Hao Zhu
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20903, USA
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Generaux G, Lakhani VV, Yang Y, Nadanaciva S, Qiu L, Riccardi K, Di L, Howell BA, Siler SQ, Watkins PB, Barton HA, Aleo MD, Shoda LKM. Quantitative systems toxicology (QST) reproduces species differences in PF-04895162 liver safety due to combined mitochondrial and bile acid toxicity. Pharmacol Res Perspect 2019; 7:e00523. [PMID: 31624633 PMCID: PMC6785660 DOI: 10.1002/prp2.523] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Revised: 08/17/2019] [Accepted: 08/19/2019] [Indexed: 01/15/2023] Open
Abstract
Many compounds that appear promising in preclinical species, fail in human clinical trials due to safety concerns. The FDA has strongly encouraged the application of modeling in drug development to improve product safety. This study illustrates how DILIsym, a computational representation of liver injury, was able to reproduce species differences in liver toxicity due to PF-04895162 (ICA-105665). PF-04895162, a drug in development for the treatment of epilepsy, was terminated after transaminase elevations were observed in healthy volunteers (NCT01691274). Liver safety concerns had not been raised in preclinical safety studies. DILIsym, which integrates in vitro data on mechanisms of hepatotoxicity with predicted in vivo liver exposure, reproduced clinical hepatotoxicity and the absence of hepatotoxicity observed in the rat. Simulated differences were multifactorial. Simulated liver exposure was greater in humans than rats. The simulated human hepatotoxicity was demonstrated to be due to the interaction between mitochondrial toxicity and bile acid transporter inhibition; elimination of either mechanism from the simulations abrogated injury. The bile acid contribution occurred despite the fact that the IC50 for bile salt export pump (BSEP) inhibition by PF-04895162 was higher (311 µmol/L) than that has been generally thought to contribute to hepatotoxicity. Modeling even higher PF-04895162 liver exposures than were measured in the rat safety studies aggravated mitochondrial toxicity but did not result in rat hepatotoxicity due to insufficient accumulation of cytotoxic bile acid species. This investigative study highlights the potential for combined in vitro and computational screening methods to identify latent hepatotoxic risks and paves the way for similar and prospective studies.
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Affiliation(s)
- Grant Generaux
- DILIsym Services Inc.Research Triangle ParkNorth Carolina
| | | | - Yuching Yang
- DILIsym Services Inc.Research Triangle ParkNorth Carolina
- Present address:
Division of PharmacometricsOffice of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchFood and Drug Administration Food and Drug AdministrationSilver SpringMaryland
| | - Sashi Nadanaciva
- Compound Safety PredictionWorldwide Medicinal ChemistryPfizer Inc.GrotonConnecticut
| | - Luping Qiu
- Investigative ToxicologyDrug Safety Research and DevelopmentPfizer Inc.GrotonConnecticut
| | - Keith Riccardi
- Pharmacokinetics, Dynamics and MetabolismMedicinal SciencesPfizer Inc.GrotonConnecticut
| | - Li Di
- Pharmacokinetics, Dynamics and MetabolismMedicinal SciencesPfizer Inc.GrotonConnecticut
| | | | - Scott Q. Siler
- DILIsym Services Inc.Research Triangle ParkNorth Carolina
| | - Paul B. Watkins
- UNC Eshelman School of PharmacyUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- UNC Institute for Drug Safety SciencesUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Hugh A. Barton
- Translational Modeling and SimulationBiomedicine DesignPfizer, Inc.GrotonConnecticut
| | - Michael D. Aleo
- Investigative ToxicologyDrug Safety Research and DevelopmentPfizer Inc.GrotonConnecticut
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Barnhill MS, Real M, Lewis JH. Latest advances in diagnosing and predicting DILI: what was new in 2017? Expert Rev Gastroenterol Hepatol 2018; 12:1033-1043. [PMID: 30111182 DOI: 10.1080/17474124.2018.1512854] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Drug-induced liver injury (DILI) remains an increasingly recognized cause of hepatotoxicity and liver failure worldwide. In 2017, we continued to learn about predicting, diagnosing, and prognosticating drug hepatotoxicity. Areas covered: In this review, we selected from over 1200 articles from 2017 to synopsize updates in DILI. There were new HLA haplotypes associated with medications including HLA-C0401 and HLA-B*14. There has been continued work with quantitative systems pharmacology, particularly with the DILIsym® initiative, which employs mathematical representations of DILI mechanisms to predict hepatotoxicity in simulated populations. Additionally, knowledge regarding microRNAs (miRNAs) continues to expand. Some new miRNAs this past year include miRNA-223 and miRNA-605. Aside from miRNAs, other biomarkers for diagnosis, prognosis, and even prediction of DILI were explored. Studies on K18, OPN, and MCSFR have correlated DILI and liver-associated death within 6 months. Conversely, a new prognostic panel using apolipoportein-A1 and haptoglobin has been proposed to predict recovery. Further study of CDH5 has also provided researchers a possible new biomarker for prediction and susceptibility to DILI. Expert commentary: Although research on DILI remains quite promising, there is yet to be a reliable, simple method to predict, diagnose, and risk assess this form of hepatotoxicity.
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Affiliation(s)
- Michele S Barnhill
- a Department of Internal Medicine , Medstar Georgetown University Hospital , Washington , DC , USA
| | - Mark Real
- a Department of Internal Medicine , Medstar Georgetown University Hospital , Washington , DC , USA
| | - James H Lewis
- b Department of Gastroenterology , Medstar Georgetown University Hospital , Washington , DC , USA
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