Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare
Abstract
:1. Introduction
2. OMICS in Advancing Clinical Decision-Making
2.1. Pharmacogenomics: Tailoring Treatment to Genetic Profiles
2.2. Challenges and Considerations in Integrating OMICS: Navigating the Road to Precision Medicine
3. Biomarkers and Molecular Diagnostics
3.1. Harnessing Biomarkers for Precision Drug Development and Treatment Optimization
3.2. Challenges in Implementing Biomarkers and Molecular Diagnostics in Precision Medicine
4. Pharmacometrics Tools: Significance and Challenges in Precision Medicine
4.1. A Triad of Precision: PKPD, PBPK, and Population PK Models in Pharmacological Insights
4.2. Challenges of Quantitative Drug Modeling
5. Data Integration and Analytics: Data-Driven Approaches in Pharmacokinetic Modeling
5.1. Unraveling Complexity: Data-Driven Pharmacokinetic Modeling in Combination Therapy
5.2. Challenges and Regulatory Considerations in Data-Driven Pharmacokinetic Modeling
6. Artificial Intelligence: Integration of Machine Learning in Pharmacometrics
6.1. Examples of ML Approaches That Can Address Unique Challenges and Opportunities within Pharmacometrics
6.2. Challenges and Future Directions
- Expanded applications of PBPK models, informing clinical study design and predicting drug interactions.
- Pediatric dosing regimen prediction to ensure safer and more effective treatments for pediatric patients.
- Utilization of PBPK models for predicting drug exposure in patients with organ impairment.
- Estimation of maternal–fetal drug disposition during pregnancy.
- Prediction of pH-mediated drug interactions using PBPK models.
- Improved predictive performance of popPK models by focusing on data adequacy.
- Integration of generic PBPK models for extrapolations and continuous updates.
7. Digital Health and Wearable Technologies
8. Clinical Trials and Study Design
9. Future Perspectives: Integration of In Silico Tools in Hospital Settings
10. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Products | Therapeutic Indication |
Cancer | |
Abecma (multiple myeloma) | |
Exkivity (lung cancer) | |
Lumakras (lung cancer) | |
Jemperli (endometrial cancer) | |
Rybrevant (lung cancer) | |
Scemblix (myeloid leukaemia) | |
Tepmetko (lung cancer) | |
Truseltiq (cholangiocarcinoma) | |
Rare Diseases | |
Amondys (muscular dystrophy) | |
Evkeeza (homozygous familial hypercholesterolaemia) | |
Nexviazyme (Pompe disease) | |
Nulibry (molybdenum cofactor deficiency) | |
Vyvgart (Myasthenia Gravis) | |
Welireg (von Hippel–Lindau) | |
Other Diseases | |
Bylvay (progressive familial intrahepatic cholestasis) | |
Cabenuva (HIV-1) | |
Leqvio (hypercholesterolaemia) |
Integration Points | Details |
---|---|
Data analysis | ML processes big data efficiently, improving patient outcomes in drug therapy. It identifies salient variables and delineates their interdependencies. |
Predictive capabilities | ML algorithms excel in predictive capabilities, aiding pharmacometrics in understanding dose–exposure relationships (pharmacokinetics) and exposure marker effects (pharmacodynamics). |
Complementing pharmacometric modelling | ML acts as a computational bridge, leveraging its flexibility to complement the complexity of principled pharmacometric modelling, resulting in synergistic effects in pharmacological applications. |
Robustness of datasets | ML implementation in pharmacometrics requires robust datasets for training and testing, capturing the distribution of intrinsic and extrinsic factors of interest. |
Overfitting | Evaluation data should not be used for training to prevent overfitting, ensuring the model generalizes well to unseen observations and doesn’t fit the training data perfectly. |
Opportunities | How to Address Them? |
---|---|
PKPD model personalization | Developing ML techniques for efficient personalization of PK/PD models to individual patients using sparse data.
|
Data integration for rare events | Designing models that integrate information from various sources (EHRs, social media, and wearable devices) to predict and manage rare adverse events not well-captured by traditional pharmacometrics models. Challenges: Scarcity of labeled data since rare events occur infrequently. |
Adaptive clinical trials that can dynamically adjust treatment regimens based on real-time data analysis | Using ML as an assisted tool for clinical trial oversight, providing efficient ways to protect patient safety, reduce trial duration, and lower costs in clinical trial oversight. Challenges: Ensuring data quality and integrity when incorporating data from multiple sources. |
Real-world evidence analysis | Using real-world evidence data to refine pharmacometrics models, accounting for patient heterogeneity, treatment variability, and long-term outcomes not adequately captured in controlled clinical trials. Challenges: Ensuring data quality and consistency. |
Interpretable AI for decision Support | Developing interpretable ML models for transparent clinical decision-making. Challenges: Balancing model complexity and transparency; difficult interpretation potentially hindering their acceptance in clinical settings. |
Uncertainty quantification | Enhancing pharmacometric models by incorporating uncertainty estimation techniques from ML, providing clinicians with confidence intervals for predictions and allowing for better risk assessment. |
Multi-modal data fusion | Investigating methods to effectively fuse data from diverse modalities, such as genomics, proteomics, and imaging data, to create comprehensive patient profiles that can better inform treatment decisions. Breakdown of the multi-modal data fusion process: (1) data collection; (2) data preprocessing; (3) feature extraction and selection; (4) data fusion; (5) model development; (6) model validation; (7) clinical application; and (8) continuous learning (as new data becomes available, the models can be updated and refined, embodying the principles of continuous learning and improvement). |
Longitudinal data analysis | Developing models for analyzing longitudinal data over extended periods to capture changes in patient response to treatments. |
Ethical and regulatory Considerations | Addressing ethical implications and regulatory challenges of incorporating ML into pharmacometrics, including issues related to data privacy, bias, and validation. |
Optimization of drug combination | Exploring ML algorithms to optimize drug combinations by predicting synergistic effects, potential adverse interactions, and tailoring treatments for individual patients. |
EHR Benefits | Integration of EHR in Healthcare |
---|---|
Information access and sharing | EHRs facilitate quick and secure access to patients’ medical information, allowing healthcare professionals to make informed decisions and order care. |
Better care management | EHRs help you better manage the care of chronic patients by enabling continuous monitoring and adjustment of treatment plans based on real-time data. |
Integration and coordination | The integration of RSE (remote sensing and earth observation) into healthcare systems allows for more efficient coordination between different healthcare providers, improving continuity of care. |
Clinical research | RSE data can be used in clinical research to identify health trends, evaluate the effectiveness of treatments, and improve evidence-based medicine. Furthermore, omics data, which encompasses genomic, transcriptomic, proteomic, and metabolomic information, plays a crucial role in precision medicine. This data enables the personalization of treatments based on the genetics and individual characteristics of each patient, improving the effectiveness of care. Omics data analysis also helps identify genetic markers of diseases, enabling early prevention and diagnosis. |
Wearable Devices | Properties, Capabilities, and Applications |
---|---|
Smartwatches | Monitor heart rate, measure blood pressure, track physical activity, count steps, monitor sleep quality, and send reminders to move, drink water or perform exercises |
Fitness trackers | Monitor steps, distance traveled, calories burned, heart rate, and even track specific exercises like running and swimming |
Glucose-monitoring devices | For people with diabetes, devices such as continuous glucose monitors (CGM) offer the ability to monitor blood glucose levels in real-time. They can send alerts when glucose levels are out of ideal range |
Portable electrocardiogram (ECG) devices | Some smartwatches can perform ECGs. They can detect abnormal heart rhythms, such as atrial fibrillation |
Sleep-monitoring devices | These devices record sleep patterns, duration, and quality. They provide insights into improving sleep habits |
Breath-monitoring devices | Can monitor respiratory rate and blood oxygen saturation. This is useful for monitoring breathing problems such as sleep apnea |
Virtual and augmented reality (VR/AR) Devices | In rehabilitation areas and therapy, VR and AR devices create virtual environments for therapeutic purposes, such as rehabilitation after injuries or strokes |
Smart glasses | These are used in medical settings for access to clinical information, real-time documentation, and telehealth |
Physiological activity-monitoring devices | In addition to the most well-known devices, some wearables monitor specific physiological activities, such as body temperature, exposure to UV light, hydration, and much more |
Wearable sensors for clinical research | In clinical research, wearable sensors are used to collect objective and accurate data about the health of patients in clinical studies, enabling a deeper understanding of different medical conditions |
Augmented reality glasses for surgery | In medicine, augmented reality glasses are used by surgeons to provide real-time information during surgical procedures, making them more accurate and safer |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Marques, L.; Costa, B.; Pereira, M.; Silva, A.; Santos, J.; Saldanha, L.; Silva, I.; Magalhães, P.; Schmidt, S.; Vale, N. Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare. Pharmaceutics 2024, 16, 332. https://doi.org/10.3390/pharmaceutics16030332
Marques L, Costa B, Pereira M, Silva A, Santos J, Saldanha L, Silva I, Magalhães P, Schmidt S, Vale N. Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare. Pharmaceutics. 2024; 16(3):332. https://doi.org/10.3390/pharmaceutics16030332
Chicago/Turabian StyleMarques, Lara, Bárbara Costa, Mariana Pereira, Abigail Silva, Joana Santos, Leonor Saldanha, Isabel Silva, Paulo Magalhães, Stephan Schmidt, and Nuno Vale. 2024. "Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare" Pharmaceutics 16, no. 3: 332. https://doi.org/10.3390/pharmaceutics16030332