Background/Objectives: Levofloxacin (LVX) is a fluoroquinolone approved for the treatment of bacterial pneumonia, sinusitis, and prostatitis. Emerging in vitro and preclinical evidence suggests that efflux transporters are involved in LVX’s target tissue site distribution. Methods: The objective of this research was to characterize tissue exposure using a physiologically based pharmacokinetic (PBPK) model to be able to make more educated choices for optimal doses using target site pharmacokinetics data. Results: The final PBPK model in humans was applied to simulate free target site concentrations of LVX in lung and prostate, linking to minimum inhibitory concentrations (MIC) to assess appropriateness of currently approved dosing regimens for infections in both tissues. The clinical PBPK model was able to reproduce total plasma as well as free lung and prostate exposure of LVX in humans. Efflux transporters participate in LVX distribution to prostatic but not pulmonary tissue. Our results show a good penetration of LVX in both tissues with unbound partition coefficient (Kp,uu) equal to 0.79 and 0.72 for lung and prostate, respectively. Since LVX penetration in lung and prostate is similar, different sensitivities of the pathogens to LVX will dictate the effectiveness of the approved therapeutic regimen in the treatment of bacterial pneumonia, sinusitis, and prostatitis. Conclusions: Our research provides relevant insight into LVX’s target site exposure in lung and prostate. When integrated with pathogen-specific susceptibility data, these findings can be applied to refine current dosing regimens and help optimize the pharmacological treatment outcomes.

Advances in biologics and model-informed drug discovery and development (MID3) are transforming the treatment of immune-mediated inflammatory diseases (IMIDs). By integrating pharmacokinetics and pharmacodynamics, MID3 enables mechanism-based, patient-centered strategies to optimize therapies, strengthen benefit-risk assessment, and advance precision medicine. Innovations such as cytokine inhibitors, bispecific antibodies, antibody-drug conjugates, and mRNA therapies have expanded treatment options across dermatology, rheumatology, gastroenterology, and respiratory medicine. The rapid development of JAK- and interleukin-targeted agents underscores the shift toward individualized, mechanistically driven therapies. At ASCPT 2025, the session "Transformative Advancements in the Treatment of IMIDs: Integrating Patient-Centric Clinical Pharmacology with Translational MID3 for Biologics" brought together experts from academia, industry, and regulatory agencies and emphasized how translational MID3 and precision pharmacology are accelerating the next generation of patient-tailored biologic therapies, with case studies in inflammatory bowel disease, systemic lupus erythematosus, and rheumatology.

Understanding exposure-response relationships is critical for the selection of an optimal drug dose that balances efficacy and safety. For simvastatin (SV), plasma concentrations may not accurately reflect target site exposure, because its pharmacologic effect is linked to intrahepatic unbound concentrations of its active form, simvastatin hydroxy acid (SVA). SVA is taken up into hepatocytes via the OATP1B1 transporter (encoded by SLCO1B1), where it is metabolized by CYP3A4. Physiological conditions such as obesity and post-Roux-en-Y gastric bypass (RYGB) surgery can alter drug disposition and enzyme activity, impacting hepatic drug exposure. This study aimed to evaluate gene-drug interaction and disease-drug interactions affecting SVA pharmacokinetics and optimize SV dosing by linking intrahepatic unbound SVA concentration to LDL-cholesterol (LDL-C) reduction using a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling approach. Simulations across doses, genotypes, and populations revealed that SLCO1B1 c.521T>C variation significantly affects plasma SVA exposure, but not hepatic SVA exposure. Obese individuals exhibited higher plasma and hepatic SVA exposure than non-obese individuals. A 20 mg dose achieved a 30-49% LDL-C reduction in obese subjects, regardless of SLCO1B1 genotype, whereas non-obese subjects may require 40 mg to achieve similar efficacy. In conclusion, systemic drug concentration or genotyping alone are insufficient to predict statin response. Instead, information on genetic and physiological variability needs to be integrated into a PBPK/PD framework to select optimal doses across diverse populations.

According to the FDA Guidance for Industry on Clinical Drug Interaction (DDI) Studies with Combined Oral Contraceptives (COCs), sponsors are expected to conduct dedicated clinical DDI studies if in vitro findings suggest weak or moderate CYP3A induction, while concomitant use of COCs with strong inducers should be avoided. The guidance further suggests that a negative DDI result for drospirenone (DRSP) may be extrapolated to other progestins that are less sensitive to CYP3A modulation, such as levonorgestrel (LNG). This approach assumes that DDI‐mediated changes in exposure directly translate into clinical efficacy across progestins. To evaluate the validity of this assumption, we established a quantitative link between dose, exposure, and response (Pearl Index [PI] and ovulation rate [OR]) via an integrated model‐based meta‐analysis, physiologically based pharmacokinetic, and pharmacokinetic/pharmacodynamic (PK/PD) modeling and simulation approach using data from 51 clinical studies in 36,040 women receiving LNG or DRSP. COCs containing LNG and DRSP were selected because they represent clinically relevant progestins at the lower and the upper end of the fraction metabolized via CYP3A4. The results of our analysis show a moderate correlation (Pearson's r = 0.52, 95% CI 0.46‐0.58, P < 0.001) between PI and OR, which enables the use of OR as an ethically measurable endpoint, even at subtherapeutic doses/exposures, to predict efficacy outcomes. They further show that DDI‐induced changes in exposure do not directly translate into clinical response. Therefore, DDIs with COCs should be interpreted in a PK/PD rather than a PK‐only context. The quantitative framework developed in this study can serve as the scientific basis to do so.

Objectives: Patients prescribed medical marijuana often have multiple long-term medications, increasing their risk of drug-drug interactions (DDIs). In vitro, Cannabidiol (CBD) and its active metabolite, 7-Hydroxycannabidiol (7-OH CBD), are known to interact with CYP enzymes through reversible and time-dependent inhibition (TDI) [1]. Incorporation of their in vitro inhibition parameters into a mechanistic static model revealed that CBD could precipitate severe DDIs with substrates of CYP3A4 and CYP2C19, and moderate DDIs with drugs metabolized by CYP1A2 and CYP2C9 [1]. Our study aims to utilize physiologically based pharmacokinetic (PBPK) modeling to further explore CBD inhibitory potential on CYP450s. Methods: Using the Simcyp Simulator (Version 22), we developed an intravenous (IV) PBPK model to characterize CBD distribution and elimination. We then constructed an oral PBPK model for CBD and 7-OH-CBD following single- and multiple-dose regimens. To explore the inhibitory effects of CBD on CYP3A4 and CYP2C19, we simulated CBD co-administration with Midazolam and Caffeine, respectively. Additionally, an independent PBPK model was developed for Clobazam and its metabolite N-desmethylclobazam to further assess the impact of CBD on CYP2C19 and CYP3A4 activity. Model predictive performance was evaluated by comparing the ratios of predicted to observed PK parameter values, with an acceptance range of 0.5 to 2-fold.Results: The base model recapitulated the plasma concentration profile observed after IV administration, achieving predicted to observed ratios close to 1. The oral PBPK model recapitulated the systemic exposure of CBD and 7-OH CBD in healthy adults within twofold of the observed values across single- and multiple-dose administrations and various dose levels. By fitting in vitro inhibition parameters to observed data, the model accurately predicted clinical DDI studies, confirming that CBD does not cause clinically significant interactions with midazolam (AUCR = 1.04) or caffeine (AUCR = 1.92). On the other hand, in vitro inhibition data successfully captured the observed clinical DDI when CBD was co-administered with Clobazam, resulting in a threefold increase in the exposure of N-desmethylclobazam due to CYP2C19 inhibition.Conclusions: While CBD exhibits inhibitory effects on major CYP enzymes in vitro, these effects are not observed clinically, except for moderate CYP2C19 inhibition. DDI signals identified by mechanistic static models often overpredicted the risk of DDI. Further optimization of in vitro DDI parameters is often needed to capture clinical data. This highlights the challenges and uncertainties in scaling drug properties from in vitro studies to human predictions, emphasizing the need for caution when using in vitro data to predict DDIs. The validated PBPK model will be extended to simulate real-world scenarios, including the impact of age, food, CYP2C19 genotype, and hepatic impairment on the magnitude of DDIs.Citations: [1] Bansal S, Maharao N, Paine MF, Unadkat JD. Predicting the Potential for Cannabinoids to Precipitate Pharmacokinetic Drug Interactions via Reversible Inhibition or Inactivation of Major Cytochromes P450. Drug Metab Dispos. 2020;48(10):1008-1017. doi:10.1124/dmd.120.000073

Initiation of Glucagon-Like Peptide-1 receptor agonists (GLP-1RA) in patients with type 2 diabetes (T2D) treated with levothyroxine may decrease the required levothyroxine dose due to weight loss or enhance levothyroxine absorption through delayed gastric emptying. These changes may cause thyroid hormone over-replacement and increased risk of atrial fibrillation/flutter (AF/Aflutter) and stroke. Our study aims to investigate the impact of GLP-1RA initiation on risks of AF/Aflutter and stroke in patients with T2D treated with levothyroxine, compared to sodium-glucose cotransporter 2 (SGLT2) inhibitors. Leveraging the target trial emulation framework, we conducted a retrospective study using observational data to emulate a new user, active comparator trial examining the effects of initiating GLP-1RA (exposure group) versus SGLT2 inhibitors (control group), with random treatment assignment emulated by propensity score matching with 1:1 ratio. We used a 15

Background: Direct oral anticoagulants (DOACs) are commonly prescribed for patients with atrial fibrillation or venous thromboembolism. Unlike Warfarin, the fixed-dose DOACs do not require close monitoring, and have minimal increase on bleeding. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) reduce weight and delay gastric emptying, witch may lead to DOAC overdosing with more adverse events. The safety of GLP-1RA initiation in patients concurrently using DOAC remains unclear. Dipeptidyl peptidase-4 (DPP4) inhibitors have minimal effect on weight and are not associated with increased bleeding risk, making them a suitable active comparator. Research Question: Is GLP-1RA initiation, compared to DPP4 inhibitors, associated with risks of stroke and bleeding in T2D patients with stable DOAC use? Methods: We emulated a prevalent new-user design trial using 15% of U.S. Medicare beneficiaries from 01/01/2011 to 12/31/2020. Patients entered the base cohort once diagnosed with T2D. Then they were randomly assigned to initiate GLP-1RA or receive DPP4 inhibitors using propensity score matching with 1:1 ratio. We included patients aged >65, with stable DOAC use (defined by MPR≥80%), and continuous Medicare enrollment during one year before the index date. Outcomes include stroke and all-cause bleeding events. Patients were followed until the first occurrence of an outcome, bariatric surgery, death, Medicare disenrollment, or 12/31/2020. Cox proportional hazards models were applied. Results: After matching, 2,002 patients were included with mean follow-up of 2 years. Mean age was 74.2 vs. 76.2 years, and 51.3% vs. 45.7% were female (GLP-1RA vs. DPP4). The risks of stroke and all-cause bleeding were both comparable between GLP-1RA and DPP4 groups (stroke: 0.68 vs. 0.64/100 person-year; HR: 1.25, 95% CI: 0.57–2.75; bleeding: 4.56 vs. 6.15/100 person-year; HR: 0.79, 95% CI: 0.59–1.05). Conclusions: In T2D patients with stable DOAC use, initiation of GLP-1RA does not significantly impact the risk of stroke or bleeding. However, due to the timeframe, liraglutide was the most prescribed GLP-1RA. Studies with latest data and larger sample size are needed to further investigate the association, especially among patients received semaglutide.

Neuropathic pain, often associated with diabetic neuropathy or nerve compression injuries, arises from damage or dysfunction in the somatosensory nervous system. Tramadol, frequently prescribed for this pain, has its fraction unbound and that of its active metabolite (M1) significantly altered by diabetes. Yet, dosing adjustments for diabetic neuropathic pain remain underexplored. This study developed a comprehensive population pharmacokinetics/pharmacodynamics (PK/PD) model for tramadol and its major metabolites, focusing on diabetes's impact on PK and PK-PD relationship to identify optimal dosing regimens. Data from patients with chronic neuropathic pain on oral tramadol were used to develop enantiomer-specific population models, considering both total and unbound concentrations. Tramadol's PK was best described by a two-compartment model with Weibull absorption and linear elimination and a one-compartment model with enterohepatic circulation and first-pass metabolism for the active M1. Simulations showed higher unbound fractions of the active M1 in patients with type 1 and type 2 diabetes. Despite a 67% and 14% reduction in the AUC of total (1R,2R)-M1 in patients with type 1 and type 2 diabetes, respectively, the AUC of unbound (1R,2R)-M1 remained consistent. The unbound concentration of the active M1 required to achieve 50% of the maximum pain reduction (IC50) was lower in patients with diabetes, indicating increased sensitivity to the drug. This model-based approach provides valuable dosing guidance, suggesting once-daily dosing treatments in patients with diabetes and twice-daily dosing for patients with neuropathic pain secondary to nerve compression mechanisms.

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.

Abstract Breakthrough bleeding (BTB) is a common side effect of hormonal contraception and is thought to impact adherence to combined oral contraceptives (COCs) but respective dose–response relationships are not yet fully understood. Therefore, the objective of this model‐based meta‐analysis (MBMA) was to establish dose–response for COCs containing different progestin/EE combinations using BTB as the pharmacodynamic endpoint. Data from 25 studies containing BTB information of 4 progestins (desogestrel, drospirenone, gestodene, and levonorgestrel) in combination with ethinyl estradiol (EE) at various dose levels was used for this analysis. The results of our MBMA show that BTB is significantly increased upon initiation of COC use but subsides over time. The time needed for BTB to return to baseline depends on the EE dose and differs marginally between progestins during the initial months of use at the same EE dose. BTB typically returns to baseline within 3 months at the highest (30 μg) dose, whereas it can take significantly longer to reestablish a regular bleeding pattern at lower EE doses (15 and 20 μg), irrespective of the progestin used. The dose–response relationships established for BTB across different progestin/EE combinations can now be used to support the selection of optimal COC dosing/treatment regimens and serve as the scientific basis for evaluating the impact of clinically relevant factors, including drug–drug interactions and demographics, on BTB.

The Center for Pharmacometrics and Systems Pharmacology (CPSP) at the University of Florida has been engaging stakeholders from industry and regulatory agencies in recent years to seek input on the requirements and expectations for next generation pharmacometricians in the workplace. The objective of this article is to share our joint perspective on identified key skills with the broader pharmacometrics community in order to initiate a collective consensus building process on how to best develop them. Pharmacometrics has evolved from a descriptive science to an applied science that is increasingly used in all phases of drug development over the last decades. Today's application for accelerating and streamlining drug development is referred to as model-informed drug development (MIDD) or, more broadly, model-informed drug discovery and development (MID3).1, 2 Due to the increasing application of MID3 approaches, rapid emergence of new data analysis and computational methods as well as increasing complexity of drug development and regulatory evaluation processes, demands for and toward pharmacometricians have been evolving over the years as well.3-5 It is no longer sufficient to master a certain tool or technical skill. Instead, these skills need to be applied in a team-based environment to solve a drug development problem. Strong technical skills and the ability to identify when pharmacometrics analyses can be used to answer a particular question are of course the foundation for every pharmacometrician. In addition to these foundational technical skills, it is our firm belief that a successful pharmacometrician should ideally: (1) be an effective communicator, (2) be able to think strategically, and (3) be able to influence team-based decisions, as outlined in Figure 1. A solid foundation of scientific knowledge (e.g., basic pharmacokinetic/pharmacodynamic [PK/PD] and pharmacology concepts) and technical pharmacometrics skills is the basis for being able to successfully apply MID3 approaches.6 Foundational technical skills include but are not limited to nonlinear mixed effects (NLME) PK/PD modeling, mechanistic PK/PD modeling, including physiologically-based pharmacokinetic (PBPK), quantitative systems pharmacology (QSP) modeling and clinical trial simulations, as shown in Table 1. We believe that a strong technical skill set comprises familiarity with pharmacometric methods, software, and programming language(s), the ability to critically assess the scientific validity of a model and its associated parameter values, as well as knowledge in pathophysiology, PKs, pharmacology, toxicology, statistics, and mathematics. These foundational technical skills are essential to influence decision-making in drug discovery and development as well as during regulatory review. With the increased use of MID3 approaches in drug discovery and early drug development,1 familiarity with emerging sciences and technologies, such as machine learning, artificial intelligence, and models relating structural properties of chemical compounds to potency/safety/PK, are becoming more important. As a consequence, the spectrum of required technical pharmacometrics skill sets has widened even further and two questions arise. To which extent do pharmacometricians need to cover the entire width of the spectrum? Is there a need for specialization among pharmacometricians? Strategic thinking entails the ability to anticipate both challenges and opportunities and to plan a course of action accordingly.8 Strategic thinking in the context of drug development requires a thorough understanding of the drug development process, applicable regulations and guidelines, and appreciation of organizational constraints (cost, time, value/risk). Obtaining this understanding requires time and is often the reason why junior pharmacometricians have difficulty leveraging MID3 approaches to streamline and accelerate drug development. Exposing junior pharmacometricians to drug development problems already during their training program (e.g., during internships in industry/regulatory agencies or joint research projects with industry/regulatory agencies) is consequently important. On the other hand, MID3 approaches provide an opportunity to facilitate strategic thinking because they allow for the integration of complex knowledge from multiple sources, explore different scenarios by quantifying assumptions, and prospectively choose the one that best meets the organization's goal (e.g., most pragmatic or has the highest probability of success). The combination of technical and strategic skills ensures that pharmacometricians can identify critical questions in drug development programs that may be answered using MID3 approaches. Strategic skills as well as the ability to influence and negotiate are essential for identifying critical questions within and across project teams, provide teams with an option for decision making based on modeling and simulation, and conveying the solutions in a manner that engages the audience. The ability to systematically integrate information and extrapolate beyond what has already been studied holds great potential for influencing decisions in drug development and regulatory approval. To broaden the impact that pharmacometricians can have on a final decision, it is important for her/him to be involved in all phases of drug discovery and development, including prospective study design, execution of the study, analyzing the data, and performing simulations for the next trial(s) along with making go/no-go decisions. Establishing this mindset early on in focused teaching and research curricula that integrate drug discovery and development with pharmacometrics is consequently beneficial. At the same time, it is important to remember that decision making is a complex process, which is affected by evidence, beliefs, assumptions and bias, a combination recurrent in common judgments. Biases in judgments reveal heuristics in our thinking under uncertainty, which can lead to severe and systematic errors. Decision making is also impacted by the way a scenario is framed. For example, a 90% chance of success would likely be perceived more favorably than a 10% risk of failure, although they are mathematically the same.9 We believe that pharmacometricians must understand this interplay and continuously educate themselves on how to maximize the impact of MID3 with the overall goal in mind (i.e., to accelerate and streamline drug development and ultimately improve patient care). At the same time, they must be willing to make trade-offs, when necessary (i.e., be adaptive and pragmatic to achieve consensus amongst team members). On top of the general increase in demand for pharmacometricians, there is an imbalance among supply, demand, and professional working opportunities between different regions of the world, resulting in geographic and academic brain drain.10 Particularly the latter poses an imminent threat to the pharmacometrics community because if this trend continues, we will soon reach a point where we will no longer have a sufficient number of academicians, particularly at the Associate and Full Professor level, that are able to train next generation pharmacometricians. To overcome these challenges, a general rethinking of traditional, siloed “business models” toward joint efforts between academia, industry, and regulatory agencies will be required. These efforts can be established at various levels, ranging from loose affiliations, such as adjunct appointments or internship opportunities for students and trainees, to structured partnerships with a dedicated logistic, financial, educational, and research infrastructure support. The latter would allow to overcome limitations of individual stakeholders (e.g., limited time for developing concepts or platform models outside the direct drug development pipeline or teaching drug development without having worked in the industry) and provide planning security (e.g., proactive workforce pipeline development, PhD and postdoctoral support for the duration of the training program, or increased utilization of large-scale databases for disease platform model development) for all parties involved. These joint efforts would also allow for the development of applied training modules, where stakeholders bring their individual strengths to the table (i.e., concepts and hands-on software training [academia], drug development context and possibly data [industry], regulatory context [regulators]). Combining forces would also allow us to stay abreast with the rapidly evolving drug development and regulatory evaluation landscape and offer training for new modalities, concepts, and analysis approaches in a timely fashion. Ideally, these partnerships would be interdisciplinary in nature to enable a broader vision to problems and ultimately spark innovation by crossing traditional knowledge boundaries. A transdisciplinary approach that integrates, for example, PBPK, machine learning, and artificial intelligence or pharmacometrics and pharmacoepidemiology, would also further a mindset of constant learning and collaboration, which is key to success in team-based environments. To facilitate the interactions, we collectively composed a list of proposed teaching and training activities needed for developing technical, strategic, as well as communication and influencing skills (Table 1). We recognize that this list, although too lengthy for any single PhD or postdoctoral fellowship program, is not all-encompassing and that the outlined activities should be tailored toward the individual trainee's educational background and working experience. We also recognize that training activities in academia may have to be complemented by downstream activities. For example, two-way sabbaticals may allow working professionals from industry or regulatory agencies to retool in academia, whereas academicians could stay abreast with latest advances in drug discovery, development, and regulatory evaluation while spending time in the industry or at the agency. Finally, we do not intend to infringe on individual faculty's freedom to train their students as they see fit, dismiss previous curricula proposal,6 or suggest that academia should take sole responsibility for the proposed teaching and training activities. We rather intend to use this proposal to spark a broader conversation among stakeholders in the pharmacometrics arena to collectively build consensus on key skills and outline viable avenues for how to best develop them. As such, we invite all stakeholders to join this conversation and welcome any constructive feedback on our proposal. The authors would like to thank Benjamin Weber for his input into the manuscript. No funding was received for this work. The authors declared no competing interests for this work.

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.

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.

Tramadol is an opioid medication used to treat moderately severe pain. Cytochrome P450 (CYP) 2D6 inhibition could be important for tramadol, as it decreases the formation of its pharmacologically active metabolite, O-desmethyltramadol, potentially resulting in increased opioid use and misuse. The objective of this study was to evaluate the impact of allosteric and competitive CYP2D6 inhibition on tramadol and O-desmethyltramadol pharmacokinetics using quinidine and metoprolol as prototypical perpetrator drugs. A physiologically based pharmacokinetic model for tramadol and O-desmethyltramadol was developed and verified in PK-Sim version 8 and linked to respective models of quinidine and metoprolol to evaluate the impact of allosteric and competitive CYP2D6 inhibition on tramadol and O-desmethyltramadol exposure. Our results show that there is a differentiated impact of CYP2D6 inhibitors on tramadol and O-desmethyltramadol based on their mechanisms of inhibition. Following allosteric inhibition by a single dose of quinidine, the exposure of both tramadol (51% increase) and O-desmethyltramadol (52% decrease) was predicted to be significantly altered after concomitant administration of a single dose of tramadol. Following multiple-dose administration of tramadol and a single-dose or multiple-dose administration of quinidine, the inhibitory effect of quinidine was predicted to be long (≈42 hours) and to alter exposure of tramadol and O-desmethyltramadol by up to 60%, suggesting that coadministration of quinidine and tramadol should be avoided clinically. In comparison, there is no predicted significant impact of metoprolol on tramadol and O-desmethyltramadol exposure. In fact, tramadol is predicted to act as a CYP2D6 perpetrator and increase metoprolol exposure, which may necessitate the need for dose separation.

Macrolide antibiotics have received criticism concerning their use and risk of treatment failure. Nevertheless, they are an important class of antibiotics and are frequently used in clinical practice for treating a variety of infections. This study sought to utilize pharmacoepidemiology methods and pharmacology principles to estimate the risk of macrolide treatment failure and quantify the influence of their pharmacokinetics on the risk of treatment failure, using clinically reported drug–drug interaction data. Using a large, commercial claims database (2006–2015), inclusion and exclusion criteria were applied to create a cohort of patients who received a macrolide for three common acute infections. Furthermore, an additional analysis examining only bacterial pneumonia events treated with macrolides was conducted. These criteria were formulated specifically to ensure treatment failure would not be expected nor influenced by intrinsic or extrinsic factors. Treatment failure rates were 6% within the common acute infections and 8% in the bacterial pneumonia populations. Regression results indicated that macrolide AUC changes greater than 50% had a significant effect on treatment failure risk, particularly for azithromycin. In fact, our results show that decreased or increased exposure change can influence failure risk, by 35% or 12%, respectively, for the acute infection scenarios. The bacterial pneumonia results were less significant with respect to the regression analyses. This integration of pharmacoepidemiology and clinical pharmacology provides a framework for utilizing real-world data to provide insight into pharmacokinetic mechanisms and support future study development related to antibiotic treatments.