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.

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).

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.

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.

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.

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.

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.

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.

Pharmacokinetic and Pharmacodynamic Analysis of Acetaminophen and Ibuprofen Dual Therapy for Patent Ductus Arteriosus Closure in Preterm Neonates at Less Than 29 Weeks of Gestation.

Patent ductus arteriosus (PDA) is a blood vessel that critically supports fetal circulation. The ductus naturally closes within a few days after birth. However, it can stay open in premature neonates for an extended period of time, which is associated with increased mortality and various co-morbidities. Ibuprofen and indomethacin are currently the only 2 drugs approved for inducing PDA closure, but both have been associated with adverse renal and bleeding events. Clinical evidence suggests that combining acetaminophen (APAP) and ibuprofen treatments can decrease the need for surgical ligation. The objective of this study was to establish a disease-drug-trial model to characterize and predict PDA closure following single and combination drug therapy with ibuprofen and/or APAP in children at less than 29 weeks of gestation. The model was informed by a comprehensive literature review. The results of our analysis suggest that ibuprofen and APAP achieve therapeutic synergy. They further suggest that the younger the preterm neonates, the higher the treatment benefit. A 5-day oral dosing regimen consisting of ibuprofen (20 mg/kg Q24h on day 1, followed by 10 mg/kg Q24h on days 2-5) plus APAP (15 mg/kg Q6h) was deemed appropriate to achieve at least 90% PDA in all preterm neonates evaluated within 1 month of life. The model can now be used to design prospective pediatric trials to evaluate optimal drug combinations for PDA closure in preterm neonates and to refine optimal dosing regimens in cohorts of differing gestational age.

Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug-Drug Interactions of Phenytoin.

Regulatory agencies worldwide expect that clinical pharmacokinetic drug-drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug's safety and efficacy. However, it is neither time nor cost efficient to test all possible DDI scenarios clinically. Phenytoin is classified by the Food and Drug Administration as a strong clinical index inducer of CYP3A4, and a moderate sensitive substrate of CYP2C9. A physiologically based pharmacokinetic (PBPK) platform model was developed using GastroPlus® to assess DDIs with phenytoin acting as the victim (CYP2C9, CYP2C19) or perpetrator (CYP3A4). Pharmacokinetic data were obtained from 15 different studies in healthy subjects. The PBPK model of phenytoin explains the contribution of CYP2C9 and CYP2C19 to the formation of 5-(4'-hydroxyphenyl)-5-phenylhydantoin. Furthermore, it accurately recapitulated phenytoin exposure after single and multiple intravenous and oral doses/formulations ranging from 248 to 900 mg, the dose-dependent nonlinearity and the magnitude of the effect of food on phenytoin pharmacokinetics. Once developed and verified, the model was used to characterize and predict phenytoin DDIs with fluconazole, omeprazole and itraconazole, i.e., simulated/observed DDI AUC ratio ranging from 0.89 to 1.25. This study supports the utility of the PBPK approach in informing drug development.

Peculiar Phosphonate Modifications of Velvet Worm Slime Revealed by Advanced Nuclear Magnetic Resonance and Mass Spectrometry.

Nature is rich with examples of highly specialized biological materials produced by organisms for functions, including defense, hunting, and protection. Along these lines, velvet worms (Onychophora) expel a protein-based slime used for hunting and defense that upon shearing and dehydration forms fibers as stiff as thermoplastics. These fibers can dissolve back into their precursor proteins in water, after which they can be drawn into new fibers, providing biological inspiration to design recyclable materials. Elevated phosphorus content in velvet worm slime was previously observed and putatively ascribed to protein phosphorylation. Here, we show instead that phosphorus is primarily present as phosphonate moieties in the slime of distantly related velvet worm species. Using high-resolution nuclear magnetic resonance (NMR), natural abundance dynamic nuclear polarization (DNP), and mass spectrometry (MS), we demonstrate that 2-aminoethyl phosphonate (2-AEP) is associated with glycans linked to large slime proteins, while transcriptomic analyses confirm the expression of 2-AEP synthesizing enzymes in slime glands. The evolutionary conservation of this rare protein modification suggests an essential functional role of phosphonates in velvet worm slime and should stimulate further study of the function of this unusual chemical modification in nature.

Modifiable statin characteristics associated with potential statin-related prescribing cascades.

STUDY OBJECTIVE: Clinicians may prescribe new medications (marker drug) to treat statin-related (index drug) adverse events, constituting a prescribing cascade. We aimed to identify modifiable statin characteristics (intensity and individual statin agents) associated with lower risk of prescribing cascades to inform clinical decisions in the presence of statin-related adverse events. DESIGN: A secondary analysis based on our previous work, a high-throughput sequence symmetry analysis screening for potential statin-related prescribing cascades. DATA SOURCE: MarketScan Commercial and Medicare Supplemental Insurance claims databases between 2005 and 2019. PATIENTS: Adults who initiated a statin between 2007 and 2018, and who were continuously enrolled in the same healthcare plan for at least 720 days before and 360 days after statin initiation. INTERVENTION: Among the previously identified 57 potential prescribing cascades, 42 statin-marker class dyad with a sample size of ≥ 500 were assessed in this study. MEASUREMENTS: We measured patients' baseline characteristics within -360 days of statin initiation and reported by modifiable statin characteristics. We also performed logistic regression and reported the adjusted odds ratios (aOR) with 95% confidence intervals (CI) of modifiable statin characteristics after adjusting for baseline characteristics. MAIN RESULTS: We identified 1,307,867 statin initiators who met the study criteria (21% elderly, 52% female). Compared with patients initiating low-intensity statins, those initiating moderate- or high-intensity statins had significantly greater odds to develop 29 (69%) prescribing cascades, including antidiabetic drugs such as dipeptidyl peptidase 4 (DPP-4) inhibitors (aOR 1.22; 95% CI, 1.11-1.35) and glucagon-like peptide-1 (GLP-1) analogs (aOR 1.31; 95% CI, 1.16-1.47), and opioids (aOR 1.18; 95% CI, 1.13-1.23). Individual statin agent selection also had a differential effect on 34 (81%) of the prescribing cascades. For example, compared with simvastatin initiators, the probability of initiating osmotically acting laxatives was significantly higher for lovastatin initiators (aOR 1.09; 95% CI, 1.03-1.15) and significantly lower in atorvastatin initiators (aOR 0.92; 95% CI, 0.89-0.94). CONCLUSION: Compared with low-intensity statins, high-intensity statins are associated with increased risk in many potential prescribing cascades, while the choice of individual statin agents affects the risk of prescribing cascades bidirectionally.

Facile Synthesis of Catechol-Containing Polyacrylamide Copolymers: Synergistic Effects of Amine, Amide and Catechol Residues in Mussel-Inspired Adhesives.

The straightforward synthesis of polyamide-derived statistical copolymers with catechol, amine, amide and hydroxy residues via free radical polymerization is presented. In particular, catechol, amine and amide residues are present in natural mussel foot proteins, enabling strong underwater adhesion due to synergistic effects where cationic residues displace hydration and ion layers, followed by strong short-rang hydrogen bonding between the catechol or primary amides and SiO2 surfaces. The present study is aimed at investigating whether such synergistic effects also exist for statistical copolymer systems that lack the sequence-defined positioning of functional groups in mussel foot proteins. A series of copolymers is established and the adsorption in saline solutions on SiO2 is determined by quartz crystal microbalance measurements and ellipsometry. These studies confirm a synergy between cationic amine groups with catechol units and primary amide groups via an increased adsorptivity and increased polymer layer thicknesses. Therefore, the free radical polymerization of catechol, amine and amide monomers as shown here may lead to simplified mussel-inspired adhesives that can be prepared with the readily scalable methods required for large-scale applications.

Informing a Comprehensive Risk Assessment of Infant Drug Exposure From Human Milk: Application of a Physiologically Based Pharmacokinetic Lactation Model for Sotalol.

Characterization of infant drug exposure through human milk is important and underexplored. Because infant plasma concentrations are not frequently collected in clinical lactation studies, modeling and simulation approaches can integrate physiology, available milk concentrations, and pediatric data to inform exposure in breastfeeding infants. A physiologically based pharmacokinetic model was built for sotalol, a renally eliminated drug, to simulate infant drug exposure from human milk. Intravenous and oral adult models were built, optimized, and scaled to an oral pediatric model for a breastfeeding-relevant age group (<2 years). Model simulations captured the data that were put aside for verification. The resulting pediatric model was applied to predict the impacts of sex, infant body size, breastfeeding frequency, age, and maternal dose (240 and 433 mg) on drug exposure during breastfeeding. Simulations suggest a minimal effect of sex or frequency on total sotalol exposure. Infants in the 90th percentile in height and weight have predicted exposures ≈20% higher than infants of the same age in the 10th percentile due to increased milk intake. The simulated infant exposures increase throughout the first 2 weeks of life and are maintained at the highest concentrations in weeks 2-4, with a consistent decrease observed as infants age. Simulations suggest that breastfeeding infants will have plasma concentrations in the lower range observed in infants administered sotalol. With further validation on additional drugs, physiologically based pharmacokinetic modeling approaches could use lactation data to a greater extent and provide comprehensive information to support decisions regarding medication use during breastfeeding.

Glycan-Presenting Coacervates Derived from Charged Poly(active esters): Preparation, Phase Behavior, and Lectin Capture.

This study presents the preparation and phase behavior of glycan-functionalized polyelectrolytes for capturing carbohydrate-binding proteins and bacteria in liquid condensate droplets. The droplets are formed by complex coacervation of poly(active ester)-derived polyanions and polycations. This approach allows for a straightforward modular introduction of charged motifs and specifically interacting units; mannose and galactose oligomers are used here as first examples. The introduction of carbohydrates has a notable effect on the phase separation and the critical salt concentration, potentially by reducing the charge density. Two mannose binding species, concanavalin A (ConA) and Escherichia coli, are shown to not only specifically bind to mannose-functionalized coacervates but also to some degree to unfunctionalized, carbohydrate-free coacervates. This suggests non-carbohydrate-specific charge-charge interactions between the protein/bacteria and the droplets. However, when mannose interactions are inhibited or when non-binding galactose-functionalized polymers are used, interactions are significantly weakened. This confirms specific mannose-mediated binding functionalization and suggests that introducing carbohydrates reduces non-specific charge-charge interactions by a so far unidentified mechanism. Overall, the presented route toward glycan-presenting polyelectrolytes enables new functional liquid condensate droplets with specific biomolecular interactions.

Disease progression joint model predicts time to type 1 diabetes onset: Optimizing future type 1 diabetes prevention studies.

Clinical trials seeking type 1 diabetes prevention are challenging in terms of identifying patient populations likely to progress to type 1 diabetes within limited (i.e., short-term) trial durations. Hence, we sought to improve such efforts by developing a quantitative disease progression model for type 1 diabetes. Individual-level data obtained from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies were used to develop a joint model that links the longitudinal glycemic measure to the timing of type 1 diabetes diagnosis. Baseline covariates were assessed using a stepwise covariate modeling approach. Our study focused on individuals at risk of developing type 1 diabetes with the presence of two or more diabetes-related autoantibodies (AAbs). The developed model successfully quantified how patient features measured at baseline, including HbA1c and the presence of different AAbs, alter the timing of type 1 diabetes diagnosis with reasonable accuracy and precision (<30% RSE). In addition, selected covariates were statistically significant (p < 0.0001 Wald test). The Weibull model best captured the timing to type 1 diabetes diagnosis. The 2-h oral glucose tolerance values assessed at each visit were included as a time-varying biomarker, which was best quantified using the sigmoid maximum effect function. This model provides a framework to quantitatively predict and simulate the time to type 1 diabetes diagnosis in individuals at risk of developing the disease and thus, aligns with the needs of pharmaceutical companies and scientists seeking to advance therapies aimed at interdicting the disease process.

Failure to predict amikacin elimination in critically ill patients with cancer based on the estimated glomerular filtration rate: applying PBPK approach in a therapeutic drug monitoring study.

PURPOSE: The aim of this work was to integrate the Therapeutic Drug Monitoring (TDM) with the model-informed precision dosing (MIPD) approach, using Physiologically-based Pharmacokinetic/Pharmacodynamic (PBPK/PD) modelling and simulation, to explore the relationship between amikacin exposure and estimated glomerular filtration rate (GFR) in critically ill patients with cancer. METHODS: In the TDM study, samples from 51 critically-ill patients with cancer treated with amikacin were analysed. Patients were stratified according to renal function based on GFR status. A full-body PBPK model with 12 organs model was developed using Simcyp V. 21, including steady-state volume of distribution of 0.21 L/kg and renal clearance of 6.9 L/h in healthy adults. PK parameters evaluated were within the 2-fold error range. RESULTS: During the validation step, predicted vs observed amikacin clearance values after single infusion dose in patients with normal renal function, mild and moderate renal impairment were 7.6 vs 8.1 L/h (7.5 mg/kg dose); 3.8 vs 4.5 L/h (1500 mg dose) and 2.2 vs 3.1 L/h (25 mg/kg dose), respectively. However, predicted vs observed amikacin clearance after a single dose infusion of 1400 mg in critically-ill patients with cancer were 1.46 vs 1.63 (P = 0.6406) L/h (severe), 2.83 vs 1.08 (P < 0.05) L/h (moderate), 4.23 vs 2.49 (P = 0.0625) L/h (mild) and 7.41 vs 3.36 (P < 0.05) L/h (normal renal function). CONCLUSION: This study demonstrated that estimated GFR did not predict amikacin elimination in critically-ill patients with cancer. Further studies are necessary to find amikacin PK covariates to optimize the pharmacotherapy in this population. Therefore, TDM of amikacin is imperative in cancer patients.

Model-Based Analysis of In Vivo Release Data of Levonorgestrel Implants: Projecting Long-Term Systemic Exposure.

Levonorgestrel (LNG) is a progestin used in many contraceptive formulations, including subcutaneous implants. There is an unmet need for developing long-acting formulations for LNG. To develop long-acting formulations, release functions need to be investigated for LNG implant. Therefore, a release model was developed and integrated into an LNG physiologically-based pharmacokinetic (PBPK) model. Utilizing a previously developed LNG PBPK model, subcutaneous administration of 150 mg LNG was implemented into the modeling framework. To mimic LNG release, ten functions incorporating formulation-specific mechanisms were explored. Release kinetic parameters and bioavailability were optimized using Jadelle® clinical trial data (n = 321) and verified using two additional clinical trials (n = 216). The First-order release and Biexponential release models showed the best fit with observed data, the adjusted R-squared (R2) value is 0.9170. The maximum released amount is approximately 50% of the loaded dose and the release rate is 0.0009 per day. The Biexponential model also showed good agreement with the data (adjusted R2 = 0.9113). Both models could recapitulate observed plasma concentrations after integration into the PBPK simulations. First-order and Biexponential release functionality may be useful in modeling subcutaneous LNG implants. The developed model captures central tendency of the observed data as well as variability of release kinetics. Future work focuses on incorporating various clinical scenarios into model simulations, including drug-drug interactions and a range of BMIs.

Population pharmacokinetic and safety analysis of ropivacaine used for erector spinae plane blocks.

INTRODUCTION: Erector spinae plane blocks have become popular for thoracic surgery. Despite a theoretically favorable safety profile, intercostal spread occurs and systemic toxicity is possible. Pharmacokinetic data are needed to guide safe dosing. METHODS: Fifteen patients undergoing thoracic surgery received continuous erector spinae plane blocks with ropivacaine 150 mg followed by subsequent boluses of 40 mg every 6 hours and infusion of 2 mg/hour. Arterial blood samples were obtained over 12 hours and analyzed using non-linear mixed effects modeling, which allowed for conducting simulations of clinically relevant dosing scenarios. The primary outcome was the Cmax of ropivacaine in erector spinae plane blocks. RESULTS: The mean age was 66 years, mean weight was 77.5 kg, and mean ideal body weight was 60 kg. The mean Cmax was 2.5 ±1.1 mg/L, which occurred at a median time of 10 (7-47) min after initial injection. Five patients developed potentially toxic ropivacaine levels but did not experience neurological symptoms. Another patient reported transient neurological toxicity symptoms. Our data suggested that using a maximum ropivacaine dose of 2.5 mg/kg based on ideal body weight would have prevented all toxicity events. Simulation predicted that reducing the initial dose to 75 mg with the same subsequent intermittent bolus dosing would decrease the risk of toxic levels to <1%. CONCLUSION: Local anesthetic systemic toxicity can occur with erector spinae plane blocks and administration of large, fixed doses of ropivacaine should be avoided, especially in patients with low ideal body weights. Weight-based ropivacaine dosing could reduce toxicity risk. TRIAL REGISTRATION NUMBER: NCT04807504; clinicaltrials.gov.