The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient’s uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the “five rights”: the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug–drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Digital Twins (DTs) are poised to transform personalized medicine by enabling real-time, multiscale simulations of individual patients. By integrating genomics, imaging, wearable sensor data, and clinical records, DTs offer a powerful platform for predictive, adaptive, and patient-centered decision-making. Recent advances have highlighted their potential across a range of clinical domains, including cardiology, oncology, pharmacogenomics, and neurology. Yet, their routine application in clinical practice remains limited, underscoring a growing translational gap between digital innovation and healthcare delivery. In this review, we explore the scientific maturity and emerging clinical use cases of DTs, while critically analyzing the systemic, regulatory, ethical, and infrastructural barriers that hinder their widespread adoption. We outline a translational roadmap that emphasizes dynamic model validation, clinician co-development, equitable data representation, and regulatory harmonization. Uniquely, we reframe DTs as cognitive tools for clinical reasoning and decision support. We further clarify translational pathways through explicit evaluation and reporting recommendations. By positioning DTs within this practical framework, we outline how responsible, inclusive, and interdisciplinary implementation can establish them as foundational elements of 21st century precision medicine.

This review analyzes the controversies surrounding lidocaine (LIDO), a widely recognized local anesthetic, by exploring its multifaceted effects on pain control in the perioperative setting. The article critically analyzes debates about lidocaine’s efficacy, safety, and optimal administration methods. While acknowledging its well-documented analgesic attributes, the text highlights the ongoing controversies in its application. The goal is to provide clinicians with a comprehensive understanding of the current discourse, enabling informed decisions about incorporating lidocaine into perioperative protocols. On the other hand, emphasizes the common uses of lidocaine and its potential role in personalized medicine. It discusses the medication’s versatility, including its application in anesthesia, chronic pain, and cardiovascular diseases. The text recognizes lidocaine’s widespread use in medical practice and its ability to be combined with other drugs, showcasing its adaptability for individualized treatments. Additionally, it explores the incorporation of lidocaine into hyaluronic acid injections and its impact on pharmacokinetics, signaling innovative approaches. The discussion centers on how lidocaine, within the realm of personalized medicine, can offer safer and more comfortable experiences for patients through tailored treatments.

In this review, we aim to discuss current information on drug interactions in the perioperative period. During this period, patients receive several drugs that may interact with each other and affect the efficacy and safety of the treatment. There are three types of drug interactions: pharmacodynamic, pharmacokinetic, and pharmaceutical. It is important to recognize that drug interactions may increase the toxicity of the drug or reduce its efficacy, increasing the risk of complications in the perioperative period. This review describes the most commonly used perioperative drugs approved by the FDA and some of the described interactions between them. Thoroughly reviewing a patient’s medication list and identifying potential interactions are essential steps in minimizing risks. Additionally, vigilant monitoring of patients during and after surgery plays a pivotal role in early detection of any signs of drug interactions. This article emphasizes the significance of addressing DDIs in the perioperative period to ensure patient well-being and advocates for the implementation of careful monitoring protocols to promptly identify and manage potential interactions.

The perioperative period, encompassing preoperative, intraoperative, and postoperative phases, is crucial for comprehensive patient care. During this time, the use of opioids and other drugs can lead to drug–drug interactions (DDIs), potentially resulting in adverse drug reactions (ADRs) that increase morbidity, mortality, and healthcare costs. This study investigates the drug–drug interactions (DDIs) between rocuronium, propofol, paracetamol, and lidocaine, focusing on the CYP-mediated metabolism of these drugs in the perioperative context, where these drugs are frequently co-administered. Using physiologically based pharmacokinetic (PBPK) modeling through the GastroPlus™ software and in vitro experiments with Hep G2 cells, we aimed to assess potential toxicities and pharmacokinetic interactions. Cellular viability assays revealed significant toxicity when lidocaine was combined with propofol and rocuronium, while paracetamol exhibited no considerable impact on viability. PBPK simulations confirmed moderate interactions with rocuronium and weak interactions with propofol but no relevant interactions with paracetamol. These findings emphasize the need for dose adjustments in perioperative settings to enhance patient safety, particularly with propofol and rocuronium, while supporting the co-administration of lidocaine and paracetamol. These findings show the importance of moving towards a personalized medicine model, adjusting the clinical use of lidocaine according to individual patient needs, thus promoting safer and more effective perioperative care and moving beyond the “one-size-fits-all” approach in anesthetic management.