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Personalized therapy in chronic viral hepatitis
Maurizia Rossana Brunetto1, Piero Colombatto, Ferruccio Bonino
1Gastroenterology and Hepatology Unit, University Hospital of Pisa, Via Paradisa 2, 56124 Cisanello, Pisa, Italy. brunetto@med-club.com
Insights
Identifying at-risk hepatitis B and C virus carriers is crucial for preventing progressive liver disease. Personalized antiviral therapy, combining molecular biology and bio-mathematical modeling, offers a promising approach for effective treatment.
Area of Science:
- Hepatology
- Virology
- Computational Biology
Background:
- Chronic hepatitis B (HBV) and C (HCV) affect over 600 million people globally.
- A significant percentage of carriers are at risk for severe liver disease and life-threatening lesions.
- Accurate identification of at-risk individuals is essential to prevent disease progression and inappropriate treatments.
Purpose of the Study:
- To review the potential of personalized antiviral therapy for chronic hepatitis B and C.
- To highlight the limitations of traditional statistical methods in managing complex host-virus interactions.
- To introduce the combined use of molecular biology and bio-mathematical modeling for optimizing treatment decisions.
Main Methods:
- Review of current literature on hepatitis B and C management.
- Discussion of the principles of personalized medicine in antiviral therapy.
- Exploration of bio-mathematical modeling for tracking viral dynamics during treatment.
Main Results:
- Standard statistical approaches are insufficient for complex host-virus interactions in chronic hepatitis.
- Personalized antiviral therapy requires consideration of individual variability, host/virus interplay, and drug resistance.
- Molecular biology combined with bio-mathematical modeling can aid clinical decision-making.
Conclusions:
- A personalized approach is necessary for effective management of chronic hepatitis B and C.
- Bio-mathematical modeling offers a dynamic tool for monitoring viral infection during therapy.
- This integrated approach has the potential to improve treatment outcomes and reduce ineffective therapies.
Abstract:
Chronic carriers of major hepatitis viruses (i.e., hepatitis B and C viruses, HBV and HCV) account for at least 600 millions people worldwide. About 50% of them are at risk for chronic hepatitis and 20-30% of patients with chronic hepatitis develop progressive liver disease and symptomatic life-threatening liver lesions. Therefore, the identification of the carrier at risk is mandatory to prevent progressive liver disease, avoiding non-appropriate treatments. The decision making has three major steps. The 1st is the identification of the patient who needs to be treated; the 2nd is the choice of the best therapeutic strategy and the most appropriate drugs and timing during the phase of infection and disease; the 3rd is the treatment optimization to reduce non effective therapy and avoid drug resistance virus mutants. This careful evaluation takes into account the individual variability, the host/virus interplays and the drug impact on viral replication with the risk of selection of resistant mutants. The complexity of the virus/host interactions, however, cannot be managed by simple mean of probabilistic statistics and/or step-wise algorithms based on population statistics. A better answer for personalized antiviral therapy may come from the combined use of molecular biology and bio-mathematical modeling that can help the medical doctor to follow the dynamic of viral infection during therapy, like the flight simulator helps the pilot. We provide a concise review of the potentials of this approach in clinical practice.
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