Clinical and Adverse Outcomes Associated With Concomitant Use of CYP2D6-Metabolized Opioids With Antidepressants in Older Nursing Home Residents : A Target Trial Emulation Study.

BACKGROUND: Limited evidence exists on the safety of pharmacokinetic interactions of cytochrome P450 (CYP) 2D6 (CYP2D6)-metabolized opioids with antidepressants among older nursing home (NH) residents. OBJECTIVE: To investigate the associations of concomitant use of CYP2D6-metabolized opioids and antidepressants with clinical outcomes and opioid-related adverse events (ORAEs). DESIGN: Retrospective cohort study using a target trial emulation framework. SETTING: 100% Medicare NH sample linked to Minimum Data Set (MDS) from 2010 to 2021. PARTICIPANTS: Long-term residents aged 65 years and older receiving CYP2D6-metabolized opioids with a disease indication for antidepressant use. INTERVENTION: Initiating CYP2D6-inhibiting versus CYP2D6-neutral antidepressants that overlapped with use of CYP2D6-metabolized opioids for 1 day or more. MEASUREMENTS: Clinical outcomes were worsening pain, physical function, and depression from baseline to quarterly MDS assessments and were analyzed using modified Poisson regression models. The ORAE outcomes included counts of pain-related hospitalizations and emergency department (ED) visits, opioid use disorder (OUD), and opioid overdose and were analyzed with negative binomial or Poisson regression models. All models were adjusted for baseline covariates via inverse probability of treatment weighting. RESULTS: Among 29 435 identified residents, use of CYP2D6-metabolized opioids concomitantly with CYP2D6-inhibiting (vs. CYP2D6-neutral) antidepressants was associated with a higher adjusted rate ratio of worsening pain (1.13 [95% CI, 1.09 to 1.17]) and higher adjusted incidence rate ratios of pain-related hospitalization (1.37 [CI, 1.19 to 1.59]), pain-related ED visit (1.49 [CI, 1.24 to 1.80]), and OUD (1.93 [CI, 1.37 to 2.73]), with no difference in physical function, depression, and opioid overdose. LIMITATION: Findings are generalizable to NH populations only. CONCLUSION: Use of CYP2D6-metabolized opioids concomitantly with CYP2D6-inhibiting (vs. CYP2D6-neutral) antidepressants was associated with worsening pain and increased risk for most assessed ORAEs among older NH residents. PRIMARY FUNDING SOURCE: National Institute on Aging.

Glycomacromolecules to Tailor Crowded and Heteromultivalent Glycocalyx Mimetics.

The glycocalyx, a complex carbohydrate layer on cell surfaces, plays a crucial role in various biological processes. Understanding native glycocalyces' complexity is challenging due to their intricate and dynamic nature. Simplified mimics of native glycocalyces offer insights into glycocalyx functions but often lack molecular precision and fail to replicate key features of the natural analogues like molecular crowding and heteromultivalency. We introduce membrane-anchoring precision glycomacromolecules synthesized via solid-phase polymer synthesis (SPPoS) and thiol-induced, light-activated controlled radical polymerization (TIRP), enabling the construction of crowded and heteromultivalent glycocalyx mimetics with varying molecular weights and densities in giant unilamellar vesicles (GUVs). The incorporation and dynamics of glycomacromolecules in the GUVs are examined via microscopy and fluorescence correlation spectroscopy (FCS) and studies on lectin-carbohydrate-mediated adhesion of GUVs reveal inhibitory and promotional adhesion effects corresponding to different glycocalyx mimetic compositions, bridging the gap between synthetic models and native analogues.

Sweet Janus Particles: Multifunctional Inhibitors of Carbohydrate-Based Bacterial Adhesion.

Escherichia coli and other bacteria use adhesion receptors, such as FimH, to attach to carbohydrates on the cell surface as the first step of colonization and infection. Efficient inhibitors that block these interactions for infection treatment are multivalent carbohydrate-functionalized scaffolds. However, these multivalent systems often lead to the formation of large clusters of bacteria, which may pose problems for clearing bacteria from the infected site. Here, we present Man-containing Janus particles (JPs) decorated on one side with glycomacromolecules to target Man-specific adhesion receptors of E. coli. On the other side, poly(N-isopropylacrylamide) is attached to the particle hemisphere, providing temperature-dependent sterical shielding against binding and cluster formation. While homogeneously functionalized particles cluster with multiple bacteria to form large aggregates, glycofunctionalized JPs are able to form aggregates only with individual bacteria. The formation of large aggregates from the JP-decorated single bacteria can still be induced in a second step by increasing the temperature and making use of the collapse of the PNIPAM hemisphere. This is the first time that carbohydrate-functionalized JPs have been derived and used as inhibitors of bacterial adhesion. Furthermore, the developed JPs offer well-controlled single bacterial inhibition in combination with cluster formation upon an external stimulus, which is not achievable with conventional carbohydrate-functionalized particles.

Short- and long-term safety of discontinuing chronic opioid therapy among older adults with Alzheimer's disease and related dementia.

BACKGROUND: Limited evidence exists on the short- and long-term safety of discontinuing versus continuing chronic opioid therapy (COT) among patients with Alzheimer's disease and related dementias (ADRD). METHODS: This cohort study was conducted among 162,677 older residents with ADRD and receipt of COT using a 100% Medicare nursing home sample. Discontinuation of COT was defined as no opioid refills for ≥90 days. Primary outcomes were rates of pain-related hospitalisation, pain-related emergency department visit, injury, opioid use disorder (OUD) and opioid overdose (OD) measured by diagnosis codes at quarterly intervals during 1- and 2-year follow-ups. Poisson regression models were fit using generalised estimating equations with inverse probability of treatment weights to model quarterly outcome rates between residents who discontinued versus continued COT. RESULTS: The study sample consisted of 218,040 resident episodes with COT; of these episodes, 180,916 residents (83%) continued COT, whereas 37,124 residents (17%) subsequently discontinued COT. Discontinuing (vs. continuing) COT was associated with higher rates of all outcomes in the first quarter, but these associations attenuated over time. The adjusted rates of injury, OUD and OD were 0, 69 and 60% lower at the 1-year follow-up and 11, 81 and 79% lower at the 2-year follow-up, respectively, for residents who discontinued versus continued COT, with no difference in the adjusted rates of pain-related hospitalisations or emergency department visits. CONCLUSIONS: The rates of adverse outcomes were higher in the first quarter but lower or non-differential at 1-year and 2-year follow-ups between COT discontinuers versus continuers among older residents with ADRD.

Advances in mechanobiological pharmacokinetic-pharmacodynamic models of osteoporosis treatment - Pathways to optimise and exploit existing therapies.

Osteoporosis (OP) is a chronic progressive bone disease which is characterised by reduction of bone matrix volume and changes in the bone matrix properties which can ultimately lead to bone fracture. The two major forms of OP are related to aging and/or menopause. With the worldwide increase of the elderly population, particularly age-related OP poses a serious health issue which puts large pressure on health care systems. A major challenge for development of new drug treatments for OP and comparison of drug efficacy with existing treatments is due to current regulatory requirements which demand testing of drugs based on bone mineral density (BMD) in phase 2 trials and fracture risk in phase 3 trials. This requires large clinical trials to be conducted and to be run for long time periods, which is very costly. This, together with the fact that there are already many drugs available for treatment of OP, makes the development of new drugs inhibitive. Furthermore, an increased trend of the use of different sequential drug therapies has been observed in OP management, such as sequential anabolic-anticatabolic drug treatment or switching from one anticatabolic drug to another. Running clinical trials for concurrent and sequential therapies is neither feasible nor practical due to large number of combinatorial possibilities. In silico mechanobiological pharmacokinetic-pharmacodynamic (PK-PD) models of OP treatments allow predictions beyond BMD, i.e. bone microdamage and degree of mineralisation can also be monitored. This will help to inform clinical drug usage and development by identifying the most promising scenarios to be tested clinically (confirmatory trials rather than exploratory only trials), optimise trial design and identify subgroups of the population that show benefit-risk profiles (both good and bad) that are different from the average patient. In this review, we provide examples of the predictive capabilities of mechanobiological PK-PD models. These include simulation results of PMO treatment with denosumab, implications of denosumab drug holidays and coupling of bone remodelling models with calcium and phosphate systems models that allows to investigate the effects of co-morbidities such as hyperparathyroidism and chronic kidney disease together with calcium and vitamin D status on drug efficacy.

Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes.

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Re-assessment of monoclonal antibodies against diclofenac for their application in the analysis of environmental waters.

The non-steroidal anti-inflammatory drug (NSAID) diclofenac (DCF) is an important environmental contaminant occurring in surface waters all over the world, because, after excretion, it is not adequately removed from wastewater in sewage treatment plants. To be able to monitor this pollutant, highly efficient analytical methods are needed, including immunoassays. In a medical research project, monoclonal antibodies against diclofenac and its metabolites had been produced. Based on this monoclonal anti-DCF antibody, a new indirect competitive enzyme-linked immunosorbent assay (ELISA) was developed and applied for environmental samples. The introduction of a spacer between diclofenac and the carrier protein in the coating conjugate led to higher sensitivity. With a test midpoint of 3 µg L-1 and a measurement range of 1-30 µg L-1, the system is not sensitive enough for direct analysis of surface water. However, this assay is quite robust against matrix influences and can be used for wastewater. Without adjustment of the calibration, organic solvents up to 5%, natural organic matter (NOM) up to 10 mg L-1, humic acids up to 2.5 mg L-1, and salt concentrations up to 6 g L-1 NaCl and 75 mg L-1 CaCl2 are tolerated. The antibody is also stable in a pH range from 3 to 12. Cross-reactivity (CR) of 1% or less was determined for the metabolites 4'-hydroxydiclofenac (4'-OH-DCF), 5-hydroxydiclofenac (5-OH-DCF), DCF lactam, and other NSAIDs. Relevant cross-reactivity occurred only with an amide derivative of DCF, 6-aminohexanoic acid (DCF-Ahx), aceclofenac (ACF) and DCF methyl ester (DCF-Me) with 150%, 61% and 44%, respectively. These substances, however, have not been found in samples. Only DCF-acyl glucuronide with a cross-reactivity of 57% is of some relevance. For the first time, photodegradation products were tested for cross-reactivity. With the ELISA based on this antibody, water samples were analysed. In sewage treatment plant effluents, concentrations in the range of 1.9-5.2 µg L-1 were determined directly, with recoveries compared to HPLC-MS/MS averaging 136%. Concentrations in lakes ranged from 3 to 4.4 ng L-1 and were, after pre-concentration, determined with an average recovery of 100%.

Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR.

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP-derived models for MIPD, offering a complementary approach to empirical model development.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare.

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.

Real-world evidence and patient preference for subcutaneous versus intravenous natalizumab in the treatment of relapsing-remitting multiple sclerosis - initial results from the observational SISTER study.

Background: The consideration of patient preference for a certain drug route of administration (RoA) plays an important role in promoting patient adherence in chronic diseases. Natalizumab is an established treatment for relapsing-remitting multiple sclerosis (RRMS) and can be administered as intravenous (IV) infusion or subcutaneous (SC) injection developed to enable a shorter and easier administration versus IV RoA. Study objectives: Primary objective is to compare patients' preference for RoA and satisfaction with SC versus IV natalizumab at baseline and subsequent visits up to 12 months. Secondary objectives include drug utilization, clinical outcomes, safety, and treatment satisfaction in a usual care setting. Design and methods: SISTER (Subcutaneous: Non-Interventional Study for Tysabri Patient Preference - Experience from Real World) is an ongoing, prospective, observational study where natalizumab is utilized according to local label. RRMS patients are included in three natalizumab cohorts: Patients switching from current IV to SC administration (switcher) and patients newly starting natalizumab on either SC or IV route (starter SC/IV). This interim analysis includes 262 patients (184 switchers, 39 SC starters, and 39 IV starters), median observation period was 9 months. Results: 80.8% IV starters and 93.9% SC starters reported at baseline that they prefer the assigned RoA. Although initial satisfaction with chosen RoA was maintained over time from baseline through Month 12 in all three cohorts, the wish for change of the current RoA after 6 and 12 months was more frequently expressed among IV starters than in either SC cohort. Consistently, six patients (23.1%) starting with IV changed their RoA from IV to SC route.Mean global treatment satisfaction according to TSQM-II score at baseline remained high in the switcher group and increased through Month 12 in both IV and SC starter cohorts. Conclusion: Based on current data, there is a trend toward patients' preference for the natalizumab SC route over the IV route, which provides valuable insights into patients' preference for natalizumab RoA in routine care and complements available data from clinical studies with real-world data on SC natalizumab. Trial registration: This observational (non-interventional) study was registered in the local German PEI register for non-interventional studies (NIS-No. 611) and in the international CTgov register (NCT05304520).

Interspecies Brain PBPK Modeling Platform to Predict Passive Transport through the Blood-Brain Barrier and Assess Target Site Disposition.

The high failure rate of central nervous system (CNS) drugs is partly associated with an insufficient understanding of target site exposure. Blood-brain barrier (BBB) permeability evaluation tools are needed to explore drugs' ability to access the CNS. An outstanding aspect of physiologically based pharmacokinetic (PBPK) models is the integration of knowledge on drug-specific and system-specific characteristics, allowing the identification of the relevant factors involved in target site distribution. We aimed to qualify a PBPK platform model to be used as a tool to predict CNS concentrations when significant transporter activity is absent and human data are sparse or unavailable. Data from the literature on the plasma and CNS of rats and humans regarding acetaminophen, oxycodone, lacosamide, ibuprofen, and levetiracetam were collected. Human BBB permeability values were extrapolated from rats using inter-species differences in BBB surface area. The percentage of predicted AUC and Cmax within the 1.25-fold criterion was 85% and 100% for rats and humans, respectively, with an overall GMFE of <1.25 in all cases. This work demonstrated the successful application of the PBPK platform for predicting human CNS concentrations of drugs passively crossing the BBB. Future applications include the selection of promising CNS drug candidates and the evaluation of new posologies for existing drugs.

Mechanistic Modeling of In Vitro Biopharmaceutic Data for a Weak Acid Drug: A Pathway Towards Deriving Fundamental Parameters for Physiologically Based Biopharmaceutic Modeling.

Mechanistic modeling of in vitro experiments using metabolic enzyme systems enables the extrapolation of metabolic clearance for in vitro-in vivo predictions. This is particularly important for successful clearance predictions using physiologically based pharmacokinetic (PBPK) modeling. The concept of mechanistic modeling can also be extended to biopharmaceutics, where in vitro data is used to predict the in vivo pharmacokinetic profile of the drug. This approach further allows for the identification of parameters that are critical for oral drug absorption in vivo. However, the routine use of this analysis approach has been hindered by the lack of an integrated analysis workflow. The objective of this tutorial is to (1) review processes and parameters contributing to oral drug absorption in increasing levels of complexity, (2) outline a general physiologically based biopharmaceutic modeling workflow for weak acids, and (3) illustrate the outlined concepts via an ibuprofen (i.e., a weak, poorly soluble acid) case example in order to provide practical guidance on how to integrate biopharmaceutic and physiological data to better understand oral drug absorption. In the future, we plan to explore the usefulness of this tutorial/roadmap to inform the development of PBPK models for BCS 2 weak bases, by expanding the stepwise modeling approach to accommodate more intricate scenarios, including the presence of diprotic basic compounds and acidifying agents within the formulation.

Exploring the impact of CYP2D6 and UGT2B7 gene-drug interactions, and CYP-mediated DDI on oxycodone and oxymorphone pharmacokinetics using physiologically-based pharmacokinetic modeling and simulation.

Oxycodone is one of the most commonly used opioids to treat moderate to severe pain. It is metabolized mainly by CYP3A4 and CYP2D6, while only a small fraction of the dose is excreted unchanged into the urine. Oxymorphone, the metabolite primarily formed by CYP2D6, has a 40- to 60-fold higher mu-opioid receptor affinity than the parent compound. While CYP2D6-mediated gene-drug-interactions (GDIs) and drug-drug interactions (DDIs) are well-studied, they only account for a portion of the variability in oxycodone and oxymorphone exposure. The combined impact of CYP2D6-mediated GDIs and DDIs, CYP3A4-mediated DDIs, and UGT2B7 GDIs is not fully understood yet and hard to study in head-to-head clinical trials given the relatively large number of scenarios. Instead, we propose the use of a physiologically-based pharmacokinetic model that integrates available information on oxycodone's metabolism to characterize and predict the impact of DDIs and GDIs on the exposure of oxycodone and its major, pharmacologically-active metabolite oxymorphone. To this end, we first developed and verified a PBPK model for oxycodone and its metabolites using published clinical data. The verified model was then applied to determine the dose-exposure relationship of oxycodone and oxymorphone stratified by CYP2D6 and UGT2B7 phenotypes respectively, and administered perpetrators of CYP-based drug interactions. Our simulations demonstrate that the combination of CYP2D6 UM and a UGT2B7Y (268) mutation may lead to a 2.3-fold increase in oxymorphone exposure compared to individuals who are phenotyped as CYP2D6 NM / UGT2B7 NM. The extent of oxymorphone exposure increases up to 3.2-fold in individuals concurrently taking CYP3A4 inhibitors, such as ketoconazole. Inhibition of the CYP3A4 pathway results in a relative increase in the partial metabolic clearance of oxycodone to oxymorphone. Oxymorphone is impacted to a higher extent by GDIs and DDIs than oxycodone. We predict oxymorphone exposure to be highest in CYP2D6 UMs/UGT2B7 PMs in the presence of ketoconazole (strong CYP3A4 index inhibitor) and lowest in CYP2D6 PMs/UGT2B7 NMs in the presence of rifampicin (strong CYP3A4 index inducer) covering a 55-fold exposure range.

Type 1 diabetes prevention clinical trial simulator: Case reports of model-informed drug development tool.

Clinical trials seeking to delay or prevent the onset of type 1 diabetes (T1D) face a series of pragmatic challenges. Despite more than 100 years since the discovery of insulin, teplizumab remains the only FDA-approved therapy to delay progression from Stage 2 to Stage 3 T1D. To increase the efficiency of clinical trials seeking this goal, our project sought to inform T1D clinical trial designs by developing a disease progression model-based clinical trial simulation tool. Using individual-level data collected from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies, we previously developed a quantitative joint model to predict the time to T1D onset. We then applied trial-specific inclusion/exclusion criteria, sample sizes in treatment and placebo arms, trial duration, assessment interval, and dropout rate. We implemented a function for presumed drug effects. To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. As an output, power was calculated, which summarizes the probability of success, showing a statistically significant difference in the time distribution until the T1D diagnosis between the two arms. Using this tool, power curves can also be generated through iterations. The web-based tool is publicly available: https://app.cop.ufl.edu/t1d/. Herein, we briefly describe the tool and provide instructions for simulating a planned clinical trial with two case studies. This tool will allow for improved clinical trial designs and accelerate efforts seeking to prevent or delay the onset of T1D.

Redefining Statin Dosage Post-Gastric Bypass: Insights from a Population Pharmacokinetics-Pharmacodynamics Link Approach.

Roux-en-Y gastric bypass (RYGB) involves creating a small stomach pouch, bypassing part of the small intestine, and rerouting the digestive tract. These alterations can potentially change the drug exposure and response. Our primary aim was to assess the impact of RYGB on the pharmacokinetics of simvastatin lactone (SV) and its active metabolite, simvastatin hydroxy acid (SVA). Ultimately, we aimed to optimize dosing for this understudied population by employing a population pharmacokinetic-pharmacodynamic link approach. The study comprised patients who had undergone RYGB surgery and individuals without a previous history of RYGB. All participants received a single oral dose of simvastatin. Plasma concentration data were analyzed with a nonlinear mixed-effect modeling approach. A parent-metabolite model with first-order absorption, 2-compartments for SV and 1-compartment for SVA, linear elimination, and enterohepatic circulation best described the data. The model was linked to the turnover pharmacodynamic model to describe the SVA inhibition on LDL-cholesterol production. Our simulations indicated that following RYGB surgery, the exposure to SV and SVA decreased by 40%. Consequently, for low-intensity statin patients, we recommend increasing the dose from 10 to 20 mg in post-RYGB patients to maintain a comparable response to that of non-operated subjects. Moderate-intensity statin patients should require increasing doses to 40 or 60 mg or the addition of a non-statin medication to achieve similar therapeutic outcomes. In conclusion, individuals post-RYGB exhibit diminished exposure to SV and may benefit from increasing the dose or adjunctive therapy with non-statin drugs to attain equivalent responses and mitigate potential adverse events.