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In recent years, advancements in molecular precision therapeutics have profoundly reshaped the understanding and treatment of complex diseases [...].In recent years, advancements in molecular precision therapeutics have profoundly reshaped the understanding and treatment of complex diseases [...].

The concept of personalized medicine (PM) has emerged as a prominent area of research to complement conventional medical approaches, grounded in the understanding that the underlying heterogeneity of individuals at the molecular, physiological, environmental exposure, and behavioral levels must be addressed by implementing interventions that are tailored or ‘personalized’ to these unique and nuanced characteristics [...]

Asthma is a common inflammatory disease of the lungs. The prevalence of asthma is increasing worldwide, and the tendency indicates that the number of asthma sufferers will soar in the coming years for several reasons, in particular, the lifestyles we have adopted that expose us to risk factors. Salbutamol is the first selective short-acting β2-agonist (SABA) used as an alternative reliever in the treatment of asthma. Its therapeutic effect is based on its potent smooth muscle relaxant properties, which allow the inhibition of bronchial smooth muscle contraction and subsequent bronchodilation. Salbutamol can be administered orally, intravenously (IV), intramuscularly (IM), subcutaneously, or by inhalation. For this reason, the pharmacokinetic (PK) parameters—absorption, distribution, metabolism, and elimination—are highly diverse and, consequently, the efficacy and adverse effects also differ between each formulation. Here, we review the pharmacological profile of different salbutamol formulations, focusing on their efficacy and adverse effects for its original application, asthma.

Depression is a prevalent, complex, and highly debilitating disease. The full comprehension of this disease is still a global challenge. Indeed, relapse, recurrency, and therapeutic resistance are serious challenges in the fight against depression. Nevertheless, abnormal functioning of the stress response, inflammatory processes, neurotransmission, neurogenesis, and synaptic plasticity are known to underlie the pathophysiology of this mental disorder. The role of oxidative stress in disease and, particularly, in depression is widely recognized, being important for both its onset and development. Indeed, excessive generation of reactive oxygen species and lack of efficient antioxidant response trigger processes such as inflammation, neurodegeneration, and neuronal death. Keeping in mind the importance of a detailed study about cellular and molecular mechanisms that are present in depression, this review focuses on the link between oxidative stress and the stress response, neuroinflammation, serotonergic pathways, neurogenesis, and synaptic plasticity’s imbalances present in depression. The study of these mechanisms is important to lead to a new era of treatment and knowledge about this highly complex disease.

Background/Objectives: Prostate and bladder cancers are significant global health challenges with increasing incidence and limited treatment options in advanced stages. Drug repurposing offers a cost-effective strategy to accelerate the development of new anticancer therapies. This study investigated the antitumor activity of the non-nucleoside reverse transcriptase inhibitors efavirenz (EFV) and etravirine (ETV) in prostate and bladder cancer models. Methods: PC-3 prostate cancer and UM-UC-5 bladder cancer cell lines were treated with EFV, ETV, or their combination. Cell viability was assessed at 24, 48, and 72 h to evaluate time and concentration-dependent effects. Wound-healing assays were used to measure cell migration, and clonogenic assays assessed long-term proliferative capacity. Results: Both EFV and ETV decreased cell viability in a time and dose-dependent manner. ETV showed greater potency in PC-3 cells, while EFV demonstrated more consistent effects in UM-UC-5 cells. Combination treatment enhanced cytotoxicity, particularly at 48 and 72 h, suggesting potential synergy. Wound-healing assays indicated impaired migration in UM-UC-5 cells treated with ETV or the EFV + ETV combination. Clonogenic assays confirmed reduced long-term proliferation in both cell lines following treatment. Conclusions: EFV and ETV exhibit selective anticancer activity in prostate and bladder cancer cells, with enhanced effects when combined. These findings support their potential as repurposed therapeutic agents and warrant further preclinical evaluation for prostate and bladder cancer therapy.

The characterization of a drug’s ADME (absorption, distribution, metabolism, and excretion) profile is crucial for accurately determining its safety and efficacy. The rising prevalence of polypharmacy has significantly increased the risk of drug-drug interactions (DDIs). These interactions can lead to altered drug exposure, potentially compromising efficacy or increasing the risk of adverse drug reactions (ADRs), thereby posing significant clinical and regulatory concerns. Traditional methods for assessing potential DDIs rely heavily on in vitro models, including enzymatic assays and transporter studies. While indispensable, these approaches have inherent limitations in scalability, cost, and ability to predict complex interactions. Recent advancements in analytical technologies, particularly the development of more sophisticated cellular models and computational modeling, have paved the way for more accurate and efficient DDI assessments. Emerging methodologies, such as organoids, physiologically based pharmacokinetic (PBPK) modeling, and artificial intelligence (AI), demonstrate significant potential in this field. A powerful and increasingly adopted approach is the integration of in vitro data with in silico modeling, which can lead to better in vitro-in vivo extrapolation (IVIVE). This review provides a comprehensive overview of both conventional and novel strategies for DDI predictions, highlighting their strengths and limitations. Equipping researchers with a structured framework for selecting optimal methodologies improves safety and efficacy evaluation and regulatory decision-making and deepens the understanding of DDIs.

Efavirenz (Sustiva®) is a first-generation non-nucleoside reverse transcriptase inhibitor (NNRTI) used to treat human immunodeficiency virus (HIV) type 1 infection or to prevent the spread of HIV. In 1998, the FDA authorized efavirenz for the treatment of HIV-1 infection. Patients formerly required three 200 mg efavirenz capsules daily, which was rapidly updated to a 600 mg tablet that only required one tablet per day. However, when given 600 mg once daily, plasma efavirenz concentrations were linked not only to poor HIV suppression but also to toxicity. Clinical data suggested that the standard dose of efavirenz could be reduced without compromising its effectiveness, resulting in a reduction in side effects and making the drug more affordable. Therefore, ENCORE1 was performed to compare the efficiency and safeness of a reduced dose of efavirenz (400 mg) with the standard dose (600 mg) plus two NRTI in antiretroviral-naïve HIV-infected individuals. Nowadays, due to the emergence of integrase strand transfer inhibitors (INSTIs), some consider that it is time to stop using efavirenz as a first-line treatment on a global scale, in the parts of the world where that is possible. Efavirenz has been a primary first-line antiviral drug for more than 15 years. However, at this moment, the best use for efavirenz could be for pre-exposure prophylaxis (PrEP) and repurposing in medicine.

Biological organisms are composed of complex subsystems that interact dynamically at different levels, with functions that are complemented to avoid system malfunctions [...].Biological organisms are composed of complex subsystems that interact dynamically at different levels, with functions that are complemented to avoid system malfunctions [...].

Background/Objectives: Gene expression-guided drug repurposing has emerged as a strategy to identify new therapy opportunities by associating disease transcriptional signatures with drug-induced gene expression profiles. This is relevant for prostate and bladder cancers, which have high molecular heterogeneity and therapy resistance limits for their standard treatment regimens. Antiretrovirals have been of great interest as repurposed candidates for these cancers due to their various effects on cancer cell pathways. The objective of this review is to assess the principles, applications, and challenges of this approach, with emphasis on antiretrovirals. Methods: This review summarizes published literature on gene expression-based drug repurposing methodologies, including signature reversion, pathway level analysis, and validation studies. Studies applying these concepts to prostate and bladder cancer were analyzed, and evidence of antiretroviral repurposing for cancer therapy was assessed based on transcriptomic alterations, pathway perturbation, and preclinical outcomes. Results: Transcriptomic-driven studies identified several drug candidates capable of modulating gene expression associated with therapy resistance, tumor progression, and cell stress responses. The anticancer effects of antiretrovirals were shown to be related to cell cycle arrest, apoptosis, metabolic alterations, and proteostasis. Nonetheless, transcriptomic responses are highly context-dependent and can be influenced by tumor subtype and experiment and treatment conditions. Off-target effects can also complicate mechanism interpretation. Conclusions: Gene expression–guided drug repurposing enables the systematic prioritization of clinically actionable candidates by matching disease and drug transcriptional signatures, but successful translation will require the integration of other omics results, careful model selection, and the development of clinically relevant biomarkers to support mechanism-informed repurposing. Translation will depend on subtype-aware signature matching, integration with complementary omics, and biomarker-backed validation to support precision deployment.

Background: Salbutamol, a short-acting β2-agonist used in asthma treatment, is available in multiple formulations, including inhalers, nebulizers, oral tablets, and intravenous, intramuscular, and subcutaneous routes. Each formulation exhibits distinct pharmacokinetic (PK) and pharmacodynamic (PD) profiles, influencing therapeutic outcomes and adverse effects. Although asthma management predominantly relies on inhaled salbutamol, understanding how these formulations interact with patient-specific characteristics could improve personalized medicine approaches, potentially uncovering the therapeutic benefits of alternative formulations for an individual patient. Herein, this study aims to analyze how covariates—such as age, weight, gender, body surface area (BSA), cytochrome P450 (CYP) expression, race, and health status—affect the therapeutic regime of orally administered salbutamol using population PK (popPK) modeling. The final model is intended as a tool to support the selection of optimal formulation and dosage regimen based on individual patient profiles. Methods: A dataset of 40 virtual patients derived from a physiologically based PK (PBPK) model of oral salbutamol was included in the popPK model. Results: A two-compartment model with first-order elimination and absorption, with a transit compartment, best described the plasma concentration–time profile following a 4 mg dose. Relationships were identified between gender and mean transit time (Mtt) and clearance (Cl), as well as the effects of weight and BSA on the volume of distribution of the central compartment (V1) and Cl, and a significant impact of health status on Cl. Conclusions: Despite current contraindications for oral salbutamol, our findings suggest that certain individuals, particularly children, may benefit from oral treatment over inhalation. We also identified individual characteristics associated with increased salbutamol toxicity risk, indicating the need for dose adjustment or alternative administration. This study further highlights the potential of popPK modeling in personalized therapy through a fully in silico approach.