Development of a Physiologically Based Pharmacokinetic Model to Predict Disease-Mediated Therapeutic Protein-Drug Interactions: Modulation of Multiple Cytochrome P450 Enzymes by Interleukin-6

Evaluating drug interaction potential from cytokine release syndrome using a physiologically based pharmacokinetic model: A case study of teclistamab

Cytokine release syndrome (CRS) was associated with teclistamab treatment in the phase I/II MajesTEC-1 study. Cytokines, especially interleukin (IL)-6, are known suppressors of cytochrome P450 (CYP) enzymes' activity. A physiologically based pharmacokinetic model evaluated the impact of IL-6 serum levels on exposure of substrates of various CYP enzymes (1A2, 2C9, 2C19, 3A4, 3A5). Two IL-6 kinetics profiles were assessed, the mean IL-6 profile with a maximum concentration (Cmax) of IL-6 (21 pg/mL) and the IL-6 profile of the patient presenting the highest IL-6 Cmax (288 pg/mL) among patients receiving the recommended phase II dose of teclistamab in MajesTEC-1. For the mean IL-6 kinetics profile, teclistamab was predicted to result in a limited change in exposure of CYP substrates (area under the curve [AUC] mean ratio 0.87-1.20). For the maximum IL-6 kinetics profile, the impact on omeprazole, simvastatin, midazolam, and cyclosporine exposure was weak to moderate (mean AUC ratios 1.90-2.23), and minimal for caffeine and s-warfarin (mean AUC ratios 0.82-1.25). Maximum change in exposure for these substrates occurred 3-4 days after step-up dosing in cycle 1. These results suggest that after cycle 1, drug interaction from IL-6 effect has no meaningful impact on CYP activities, with minimal or moderate impact on CYP substrates. The highest risk of drug interaction is expected to occur during step-up dosing up to 7 days after the first treatment dose (1.5 mg/kg subcutaneously) and during and after CRS.

Warfarin-A natural anticoagulant: A review of research trends for precision medication

BACKGROUND

Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability.

PURPOSE

The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage.

METHODS

A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature.

RESULTS

Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database.

CONCLUSION

Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.

Development of a Physiologically Based Pharmacokinetic Population Model for Diabetic Patients and its Application to Understand Disease-drug-drug Interactions

INTRODUCTION

The activity changes of cytochrome P450 (CYP450) enzymes, along with the complicated medication scenarios in diabetes mellitus (DM) patients, result in the unanticipated pharmacokinetics (PK), pharmacodynamics (PD), and drug-drug interactions (DDIs). Physiologically based pharmacokinetic (PBPK) modeling has been a useful tool for assessing the influence of disease status on CYP enzymes and the resulting DDIs. This work aims to develop a novel diabetic PBPK population model to facilitate the prediction of PK and DDI in DM patients.

METHODS

First, mathematical functions were constructed to describe the demographic and non-CYP physiological characteristics specific to DM, which were then incorporated into the PBPK model to quantify the net changes in CYP enzyme activities by comparing the PK of CYP probe drugs in DM versus non-DM subjects.

RESULTS

The results show that the enzyme activity is reduced by 32.3% for CYP3A4/5, 39.1% for CYP2C19, and 27% for CYP2B6, while CYP2C9 activity is enhanced by 38% under DM condition. Finally, the diabetic PBPK model was developed through integrating the DM-specific CYP activities and other parameters and was further used to perform PK simulations under 12 drug combination scenarios, among which 3 combinations were predicted to result in significant PK changes in DM, which may cause DDI risks in DM patients.

CONCLUSIONS

The PBPK modeling applied herein provides a quantitative tool to assess the impact of disease factors on relevant enzyme pathways and potential disease-drug-drug-interactions (DDDIs), which may be useful for dosing regimen optimization and minimizing the DDI risks associated with the treatment of DM.

Physiologically Based Pharmacokinetic Modeling to Investigate the Disease-Drug-Drug Interactions between Voriconazole and Nirmatrelvir/Ritonavir in COVID-19 Patients with CYP2C19 Phenotypes

Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis superinfection with cytokine storm is associated with increased mortality. This study aimed to establish a physiologically-based pharmacokinetic (PK) model to investigate the disease-drug-drug interactions between voriconazole and nirmatrelvir/ritonavir in patients with COVID-19 with elevated interleukin-6 (IL-6) levels carrying various CYP2C19 phenotypes. The model was constructed and validated using PK data on voriconazole, ritonavir, and IL-6, and was subsequently verified against clinical data from 78 patients with COVID-19. As a result, the model predicted voriconazole, ritonavir, and IL-6 PK parameters and drug-drug interaction-related fold changes in healthy subjects and patients with COVID-19 with acceptable prediction error, demonstrating its predictive capability. Simulations indicated ritonavir could increase voriconazole exposure to CYP2C19 intermediate and poor metabolizers rather than decrease it, in contrast to what is indicated in the drug package insert. However, the predicted ritonavir exposures were comparable across subjects. In patients with COVID-19, both ritonavir and IL-6 increased voriconazole trough concentrations, which may lead to CYP2C19 phenotype-dependent overexposure. In conclusion, COVID-19-induced IL-6 elevation and ritonavir increased voriconazole exposure, and the magnitude of interactions was influenced by CYP2C19 phenotype. Thus, caution is warranted when prescribing voriconazole concomitantly with Paxlovid in patients with COVID-19.

Evaluation of the potential impact on pharmacokinetics of various cytochrome P450 substrates of increasing IL-6 levels following administration of the T-cell bispecific engager glofitamab

Glofitamab is a novel T cell bispecific antibody developed for treatment of relapsed-refractory diffuse large B cell lymphoma and other non-Hodgkin's lymphoma indications. By simultaneously binding human CD20-expressing tumor cells and CD3 on T cells, glofitamab induces tumor cell lysis, in addition to T-cell activation, proliferation, and cytokine release. Here, we describe physiologically-based pharmacokinetic (PBPK) modeling performed to assess the impact of glofitamab-associated transient increases in interleukin 6 (IL-6) on the pharmacokinetics of several cytochrome P450 (CYP) substrates. By refinement of a previously described IL-6 model and inclusion of in vitro CYP suppression data for CYP3A4, CYP1A2, and 2C9, a PBPK model was established in Simcyp to capture the induced IL-6 levels seen when glofitamab is administered at the intended dose and dosing regimen. Following model qualification, the PBPK model was used to predict the potential impact of CYP suppression on exposures of various CYP probe substrates. PBPK analysis predicted that, in the worst-case, the transient elevation of IL-6 would increase exposures of CYP3A4, CYP2C9, and CYP1A2 substrates by less than or equal to twofold. Increases for CYP3A4, CYP2C9, and CYP1A2 substrates were projected to be 1.75, 1.19, and 1.09-fold following the first administration and 2.08, 1.28, and 1.49-fold following repeated administrations. It is recommended that there are no restrictions on concomitant treatment with any other drugs. Consideration may be given for potential drug-drug interaction during the first cycle in patients who are receiving concomitant CYP substrates with a narrow therapeutic index via monitoring for toxicity or for drug concentrations.

Assessment of CYP3A-mediated drug interaction via cytokine (IL-6) elevation for mosunetuzumab using physiologically-based pharmacokinetic modeling

Mosunetuzumab is a CD3/CD20 bispecific antibody. As an on-target effect, transient elevation of interleukin-6 (IL-6) occurs in early treatment cycles. A physiologically-based pharmacokinetic (PBPK) model was developed to assess potential drug interaction caused by IL-6 enzyme suppression on cytochrome P450 3A (CYP3A) during mosunetuzumab treatment. The model's performance in predicting IL-6 CYP3A suppression and subsequent drug-drug interactions (DDIs) was verified using existing clinical data of DDIs caused by chronic and transient IL-6 elevation. Sensitivity analyses were performed for a complete DDI risk assessment. The IL-6 concentration- and time-dependent CYP3A suppression during mosunetuzumab treatment was simulated using PBPK model with incorporation of in vitro IL-6 inhibition data. At clinically approved doses/regimens, the DDI at maximum CYP3A suppression was predicted to be a midazolam maximum drug concentration in plasma (Cmax ) and area under the plasma drug concentration-time curve (AUC) ratio of 1.17 and 1.37, respectively. At the 95th percentile of IL-6 concentration level or when gut CYP3A suppression was considered, the predicted DDI risk for mosunetuzumab remained low (<2-fold). The PBPK-based DDI predictions informed the mosunetuzumab product label to monitor, in early cycles, the concentrations and toxicities for sensitive CYP3A substrates with narrow therapeutic windows.

Therapeutic Protein Drug Interactions: A White Paper From the International Consortium for Innovation and Quality in Pharmaceutical Development

Typically, therapeutic proteins (TPs) have a low risk for eliciting meaningful drug interactions (DIs). However, there are select instances where TP drug interactions (TP-DIs) of clinical concern can occur. This white paper discusses the various types of TP-DIs involving mechanisms such as changes in disease state, target-mediated drug disposition, neonatal Fc receptor (FcRn), or antidrug antibodies formation. The nature of TP drug interaction being investigated should determine whether the examination is conducted as a standalone TP-DI study in healthy participants, in patients, or assessed via population pharmacokinetic analysis. DIs involving antibody-drug conjugates are discussed briefly, but the primary focus here will be DIs involving cytokine modulation. Cytokine modulation can occur directly by certain TPs, or indirectly due to moderate to severe inflammation, infection, or injury. Disease states that have been shown to result in indirect disease-DIs that are clinically meaningful have been listed (i.e., typically a twofold change in the systemic exposure of a coadministered sensitive cytochrome P450 substrate drug). Type of disease and severity of inflammation should be the primary drivers for risk assessment for disease-DIs. While more clinical inflammatory marker data needs to be collected, the use of two or more clinical inflammatory markers (such as C-reactive protein, albumin, or interleukin 6) may help broadly categorize whether the predicted magnitude of inflammatory disease-DI risk is negligible, weak, or moderate to strong. Based on current knowledge, clinical DI studies are not necessary for all TPs, and should no longer be conducted in certain disease patient populations such as psoriasis, which do not have sufficient systemic inflammation to cause a meaningful indirect disease-DI.

Physiologically-based pharmacokinetic modeling of immunoglobulin and antibody coadministration in patients with primary human immunodeficiency

Intravenous immunoglobulin (IVIG) (2000 mg/kg) increased the clearance of the mouse monoclonal antibody 7E3, directed against platelet integrin IIb/IIIa (alpha IIb beta 3, CD41/CD61) in rodents. We wanted to investigate the effect of IVIG on clearance of monoclonal antibodies in humans as there is extremely limited data regarding this interaction in the literature. Using the tyrosine protein kinase KIT anti-cluster of differentiation 117 (c-Kit) humanized monoclonal antibody (JSP191) as a case study, we used physiologically-based pharmacokinetic (PBPK) modeling to evaluate the pharmacokinetic interaction between monoclonal antibodies and IVIG at doses (300-600 mg/kg) administered to patients with primary human immunodeficiency (PI). We first characterized the interaction between monoclonal antibodies and IVIG in PK-Sim®/MoBi® using published literature data, including the following: IVIG plus 7E3 in mice and rats and IVIG plus the human anti-C5 monoclonal antibody tesidolumab in adults with end-stage renal disease. We next developed a PBPK model using digitized data for JSPI91 alone in older adults with myelodysplastic syndrome and acute myeloid leukemia and in pediatric patients with severe combined immunodeficiency (SCID). Finally, we simulated the impact of IVIG (300-2000 mg/kg) coadministration with JSP191 on the area under the curve of JSP191 in patients with SCID. Model predictions were within 1.5-fold of observed values for 7E3 plus IVIG and tesidolumab plus IVIG as well as for JSP191 administered alone. Based on our simulations, IVIG doses ≥500 mg exceeded the 80%-125% no-effect boundaries. IVIG treatment with monoclonal antibodies in patients with PI may result in a clinically significant interaction depending on the IVIG dose administered and the exposure-response relationship for the specific monoclonal antibody.

The Cytokine Release Syndrome and/or the Proinflammatory Cytokines as Underlying Mechanisms of Downregulation of Drug Metabolism and Drug Transport: A Systematic Review of the Clinical Pharmacokinetics of Victim Drugs of this Drug-Disease Interaction Under Different Clinical Conditions

BACKGROUND AND OBJECTIVE

An ever-growing body of evidence supports the impact of cytokine modulation on the patient's phenotypic drug response. The aim of this systematic review was to analyze the clinical studies that assessed the pharmacokinetics of victim drugs of this drug-disease interaction in the presence of different scenarios of cytokine modulation in comparison with baseline conditions.

METHODS

We conducted a systematic review by searching the PubMed-MEDLINE database from inception until February 2022 to retrieve prospective and/or retrospective observational studies, population pharmacokinetic studies, phase I studies, and/or case series/reports that investigated the impact of cytokine modulation on the pharmacokinetic behavior of victim drugs. Only studies providing quantitative pharmacokinetic data of victim drugs by comparing normal status versus clinical conditions with documented cytokine modulation or by assessing the influence of anti-inflammatory biological agents on metabolism and/or transport of victim drugs were included.

RESULTS

Overall, 26 studies were included. Rheumatoid arthritis (6/26; 23.1%) and sepsis (5/26; 19.2%) were the two most frequently investigated pro-inflammatory clinical scenarios. The victim drug most frequently assessed was midazolam (14/26; 53.8%; as a probe for cytochrome P450 [CYP] 3A4). Cytokine modulation showed a moderate inhibitory effect on CYP3A4-mediated metabolism (area under the concentration-time curve increase and/or clearance decrease between 1.98-fold and 2.59-fold) and a weak-to-moderate inhibitory effect on CYP1A2, CYP2C9, and CYP2C19-mediated metabolism (in the area under the concentration-time curve increase or clearance decrease between 1.29-fold and 1.97-fold). Anti-interleukin-6 agents showed remarkable activity in counteracting downregulation of CYP3A4-mediated activity (increase in the area under the concentration-time curve between 1.75-fold and 2.56-fold).

CONCLUSIONS

Cytokine modulation may cause moderate or weak-to-moderate downregulation of metabolism/transport of victim drugs, and this may theoretically have relevant clinical consequences.

Influence of Inflammation on Cytochromes P450 Activity in Adults: A Systematic Review of the Literature

Background: Available in-vitro and animal studies indicate that inflammation impacts cytochromes P450 (CYP) activity via multiple and complex transcriptional and post-transcriptional mechanisms, depending on the specific CYP isoforms and the nature of inflammation mediators. It is essential to review the current published data on the impact of inflammation on CYP activities in adults to support drug individualization based on comorbidities and diseases in clinical practice. Methods: This systematic review was conducted in PubMed through 7th January 2021 looking for articles that investigated the consequences of inflammation on CYP activities in adults. Information on the source of inflammation, victim drugs (and CYPs involved), effect of disease-drug interaction, number of subjects, and study design were extracted. Results: The search strategy identified 218 studies and case reports that met our inclusion criteria. These articles were divided into fourteen different sources of inflammation (such as infection, autoimmune diseases, cancer, therapies with immunomodulator…). The impact of inflammation on CYP activities appeared to be isoform-specific and dependent on the nature and severity of the underlying disease causing the inflammation. Some of these drug-disease interactions had a significant influence on drug pharmacokinetic parameters and on clinical management. For example, clozapine levels doubled with signs of toxicity during infections and the concentration ratio between clopidogrel's active metabolite and clopidogrel is 48-fold lower in critically ill patients. Infection and CYP3A were the most cited perpetrator of inflammation and the most studied CYP, respectively. Moreover, some data suggest that resolution of inflammation results in a return to baseline CYP activities. Conclusion: Convincing evidence shows that inflammation is a major factor to be taken into account in drug development and in clinical practice to avoid any efficacy or safety issues because inflammation modulates CYP activities and thus drug pharmacokinetics. The impact is different depending on the CYP isoform and the inflammatory disease considered. Moreover, resolution of inflammation appears to result in a normalization of CYP activity. However, some results are still equivocal and further investigations are thus needed.

Prediction of cytochromes P450 3A and 2C19 modulation by both inflammation and drug interactions using physiologically based pharmacokinetics

Xenobiotics can interact with cytochromes P450 (CYPs), resulting in drug-drug interactions, but CYPs can also contribute to drug-disease interactions, especially in the case of inflammation, which downregulates CYP activities through pretranscriptional and posttranscriptional mechanisms. Interleukin-6 (IL-6), a key proinflammatory cytokine, is mainly responsible for this effect. The aim of our study was to develop a physiologically based pharmacokinetic (PBPK) model to foresee the impact of elevated IL-6 levels in combination with drug interactions with esomeprazole on CYP3A and CYP2C19. Data from a cohort of elective hip surgery patients whose CYP3A and CYP2C19 activities were measured before and after surgery were used to validate the accurate prediction of the developed models. Successive steps were to fit models for IL-6, esomeprazole, and omeprazole and its metabolite from the literature and to validate them. The models for midazolam and its metabolite were obtained from the literature. When appropriate, a correction factor was applied to convert drug concentrations from whole blood to plasma. Mean ratios between simulated and observed areas under the curve for omeprazole/5-hydroxy omeprazole, esomeprazole, and IL-6 were 1.53, 1.06, and 0.69, respectively, indicating an accurate prediction of the developed models. The impact of IL-6 and esomeprazole on the exposure to CYP3A and CYP2C19 probe substrates and respective metabolites were correctly predicted. Indeed, the ratio between predicted and observed mean concentrations were <2 for all observations (ranging from 0.51 to 1.7). The impact of IL-6 and esomeprazole on CYP3A and CYP2C19 activities after a hip surgery were correctly predicted with the developed PBPK models.

Utilization of physiologically-based pharmacokinetic model to assess disease-mediated therapeutic protein-disease-drug interaction in immune-mediated inflammatory diseases

It is known that interleukin-6 (IL-6) can significantly modulate some key drug-metabolizing enzymes, such as phase I cytochrome P450s (CYPs). In this study, a physiologically-based pharmacokinetic (PBPK) model was developed to assess CYPs mediated therapeutic protein drug interactions (TP-DIs) in patients with immune-mediated inflammatory diseases (IMIDs) with elevated systemic IL-6 levels when treated by anti-IL-6 therapies. Literature data of IL-6 levels in various diseases were incorporated in SimCYP to construct respective virtual patient populations. The modulation effects of systemic IL-6 level and local IL-6 level in the gastrointestinal tract (GI) on CYPs activities were assessed. Upon blockade of the IL-6 signaling pathway by an anti-IL-6 treatment, the area under plasma concentration versus time curves (AUCs) of S-warfarin, omeprazole, and midazolam were predicted to decrease by up to 40%, 42%, and 46%, respectively. In patients with Crohn's disease and ulcerative colitis treated with an anti-IL-6 therapy, the lowering of the elevated IL-6 levels in the local GI tissue were predicted to result in further decreases in AUCs of those CYP substrates. The propensity of TP-DIs under comorbidity conditions, such as in patients with cancer with IMID, were also explored. With further validation with relevant clinical data, this PBPK model may provide an in silico way to quantify the magnitude of potential TP-DI in patients with elevated IL-6 levels when an anti-IL-6 therapeutic is used with concomitant small-molecule drugs. This model may be further adapted to evaluate the CYP modulation effect by other therapeutic modalities, which would significantly alter levels of proinflammatory cytokines during the treatment period.

Physiologically Based Pharmacokinetic Modelling to Investigate the Impact of the Cytokine Storm on CYP3A Drug Pharmacokinetics in COVID-19 Patients

Patients with coronavirus disease 2019 (COVID‐19) may experience a cytokine storm with elevated interleukin‐6 (IL‐6) levels in response to severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2). IL‐6 suppresses hepatic enzymes, including CYP3A; however, the effect on drug exposure and drug‐drug interaction magnitudes of the cytokine storm and resulting elevated IL‐6 levels have not been characterized in patients with COVID‐19. We used physiologically‐based pharmacokinetic (PBPK) modeling to simulate the effect of inflammation on the pharmacokinetics of CYP3A metabolized drugs. A PBPK model was developed for lopinavir boosted with ritonavir (LPV/r), using clinically observed data from people living with HIV (PLWH). The inhibition of CYPs by IL‐6 was implemented by a semimechanistic suppression model and verified against clinical data from patients with COVID‐19, treated with LPV/r. Subsequently, the verified model was used to simulate the effect of various clinically observed IL‐6 levels on the exposure of LPV/r and midazolam, a CYP3A model drug. Clinically observed LPV/r concentrations in PLWH and patients with COVID‐19 were predicted within the 95% confidence interval of the simulation results, demonstrating its predictive capability. Simulations indicated a twofold higher LPV exposure in patients with COVID‐19 compared with PLWH, whereas ritonavir exposure was predicted to be comparable. Varying IL‐6 levels under COVID‐19 had only a marginal effect on LPV/r pharmacokinetics according to our model. Simulations showed that a cytokine storm increased the exposure of the CYP3A paradigm substrate midazolam by 40%. Our simulations suggest that CYP3A metabolism is altered in patients with COVID‐19 having increased cytokine release. Caution is required when prescribing narrow therapeutic index drugs particularly in the presence of strong CYP3A inhibitors.

Modeling Approach to Predict the Impact of Inflammation on the Pharmacokinetics of CYP2C19 and CYP3A4 Substrates

PURPOSE

For decades, inflammation has been considered a cause of pharmacokinetic variability, mainly in relation to the inhibitory effect of pro-inflammatory cytokines on the expression level and activity of cytochrome P450 (CYP). In vitro and clinical studies have shown that two major CYPs, CYP2C19 and CYP3A4, are both impaired. The objective of the present study was to quantify the impact of the inflammatory response on the activity of both CYPs in order to predict the pharmacokinetic profile of their substrates according to systemic C-reactive protein (CRP).

METHODS

The relationships between CRP concentration and both CYPs activities were estimated and validated using clinical data first on midazolam then on voriconazole. Finally, clinical data on omeprazole were used to validate the findings. For each substrate, a physiologically based pharmacokinetics model was built using a bottom-up approach, and the relationships between CRP level and CYP activities were estimated by a top-down approach. After incorporating the respective relationships, we compared the predictions and observed drug concentrations.

RESULTS

Changes in pharmacokinetic profiles and parameters induced by inflammation seem to be captured accurately by the models.

CONCLUSIONS

These findings suggest that the pharmacokinetics of CYP2C19 and CYP3A4 substrates can be predicted depending on the CRP concentration.

Interactions of janus kinase inhibitors with drug transporters and consequences for pharmacokinetics and toxicity

Introduction: Janus kinase inhibitors (JAKinibs) constitute an emerging and promising pharmacological class of anti-inflammatory or anti-cancer drugs, used notably for the treatment of rheumatoid arthritis and some myeloproliferative neoplasms.Areas covered: This review provides an overview of the interactions between marketed JAKinibs and major uptake and efflux drug transporters. Consequences regarding pharmacokinetics, drug-drug interactions and toxicity are summarized.Expert opinion: JAKinibs interact in vitro with transporters in various ways, as inhibitors or as substrates of transporters or as regulators of transporter expression. This may theoretically result in drug-drug interactions (DDIs), with JAKinibs acting as perpetrators or as victims, or in toxicity, via impairment of thiamine transport. Clinical significance in terms of DDIs for JAKinib-transporter interactions remains however poorly documented. In this context, the in vivo unbound concentration of JAKinibs is likely a key parameter to consider for evaluating the clinical relevance of JAKinibs-mediated transporter inhibition. Additionally, the interplay with drug metabolism as well as possible interactions with transporters of emerging importance and time-dependent inhibition have to be taken into account. The role drug transporters may play in controlling cellular JAKinib concentrations and efficacy in target cells is also an issue of interest.

Informing Development of Bispecific Antibodies Using Physiologically Based Pharmacokinetic-Pharmacodynamic Models: Current Capabilities and Future Opportunities

Antibody therapeutics continue to represent a significant portion of the biotherapeutic pipeline, with growing promise for bispecific antibodies (BsAbs). BsAbs can target 2 different antigens at the same time, such as simultaneously binding tumor-cell receptors and recruiting cytotoxic immune cells. This simultaneous engagement of 2 targets can be potentially advantageous, as it may overcome disadvantages posed by a monotherapy approach, like the development of resistance to treatment. Combination therapy approaches that modulate 2 targets simultaneously offer similar advantages, but BsAbs are more efficient to develop. Unlike combination approaches, BsAbs can facilitate spatial proximity of targets that may be necessary to induce the desired effect. Successful development of BsAbs requires understanding antibody formatting and optimizing activity for both targets prior to clinical trials. To realize maximal efficacy, special attention is required to fully define pharmacokinetic (PK)/pharmacodynamic (PD) relationships enabling selection of dose and regimen. The application of physiologically based pharmacokinetics (PBPK) has been evolving to inform the development of novel treatment modalities such as bispecifics owing to the increase in our understanding of pharmacology, utility of multiscale models, and emerging clinical data. In this review, we discuss components of PBPK models to describe the PK characteristics of BsAbs and expand the discussion to integration of PBPK and PD models to inform development of BsAbs. A framework that can be adopted to build PBPK-PD models to inform the development of BsAbs is also proposed. We conclude with examples that highlight the application of PBPK-PD and share perspectives on future opportunities for this emerging quantitative tool.

Cytochrome P450-mediated drug interactions in COVID-19 patients: Current findings and possible mechanisms

At the end of 2019, the entire world has witnessed the birth of a new member of coronavirus family in Wuhan, China. Ever since, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has swiftly invaded every corner on the planet. By the end of April 2020, almost 3.5 million cases have been reported worldwide, with a death toll of about 250,000 deaths. It is currently well-recognized that patient’s immune response plays a pivotal role in the pathogenesis of Coronavirus Disease 2019 (COVID-19). This inflammatory element was evidenced by its elevated mediators that, in severe cases, reach their peak in a cytokine storm. Together with the reported markers of liver injury, such hyperinflammatory state may trigger significant derangements in hepatic cytochrome P450 metabolic machinery, and subsequent modulation of drug clearance that may result in unexpected therapeutic/toxic response. We hypothesize that COVID-19 patients are potentially vulnerable to a significant disease-drug interaction, and therefore, suitable dosing guidelines with therapeutic drug monitoring should be implemented to assure optimal clinical outcomes.

Current trends in drug metabolism and pharmacokinetics

Pharmacokinetics (PK) is the study of the absorption, distribution, metabolism, and excretion (ADME) processes of a drug. Understanding PK properties is essential for drug development and precision medication. In this review we provided an overview of recent research on PK with focus on the following aspects: (1) an update on drug-metabolizing enzymes and transporters in the determination of PK, as well as advances in xenobiotic receptors and noncoding RNAs (ncRNAs) in the modulation of PK, providing new understanding of the transcriptional and posttranscriptional regulatory mechanisms that result in inter-individual variations in pharmacotherapy; (2) current status and trends in assessing drug-drug interactions, especially interactions between drugs and herbs, between drugs and therapeutic biologics, and microbiota-mediated interactions; (3) advances in understanding the effects of diseases on PK, particularly changes in metabolizing enzymes and transporters with disease progression; (4) trends in mathematical modeling including physiologically-based PK modeling and novel animal models such as CRISPR/Cas9-based animal models for DMPK studies; (5) emerging non-classical xenobiotic metabolic pathways and the involvement of novel metabolic enzymes, especially non-P450s. Existing challenges and perspectives on future directions are discussed, and may stimulate the development of new research models, technologies, and strategies towards the development of better drugs and improved clinical practice.

Prediction of Atorvastatin Pharmacokinetics in High-Fat Diet and Low-Dose Streptozotocin-Induced Diabetic Rats Using a Semiphysiologically Based Pharmacokinetic Model Involving Both Enzymes and Transporters

Atorvastatin is a substrate of cytochrome P450 3a (CYP3a), organic anion-transporting polypeptides (OATPs), breast cancer-resistance protein (BCRP), and P-glycoprotein (P-gp). We aimed to develop a semiphysiologically based pharmacokinetic (semi-PBPK) model involving both enzyme and transporters for predicting the contributions of altered function and expression of CYP3a and transporters to atorvastatin transport in diabetic rats by combining high-fat diet feeding and low-dose streptozotocin injection. Atorvastatin metabolism and transport parameters comes from in situ intestinal perfusion, primary hepatocytes, and intestinal or hepatic microsomes. We estimated the expressions and functions of these proteins and their contributions. Diabetes increased the expression of hepatic CYP3a, OATP1b2, and P-gp but decreased the expression of intestinal CYP3a, OATP1a5, and P-gp. The expression and function of intestinal BCRP were significantly decreased in 10-day diabetic rats but increased in 22-day diabetic rats. Based on alterations in CYP3a and transporters by diabetes, the developed semi-PBPK model was successfully used to predict atorvastatin pharmacokinetics after oral and intravenous doses to rats. Contributions to oral atorvastatin PK were intestinal OATP1a5 < intestinal P-gp < intestinal CYP3a < hepatic CYP3a < hepatic OATP1b2 < intestinal BRCP. Contributions of decreased expression and function of intestinal CYP3a and P-gp by diabetes to oral atorvastatin plasma exposure were almost attenuated by increased expression and function of hepatic CYP3a and OATP1b2. Opposite alterations in oral plasma atorvastatin exposure in 10- and 22-day diabetic rats may be explained by altered intestinal BCRP. In conclusion, the altered atorvastatin pharmacokinetics by diabetes was the synergistic effects of altered intestinal or hepatic CYP3a and transporters and could be predicted using the developed semi-PBPK.

Simulating the Impact of Elevated Levels of Interleukin-6 on the Pharmacokinetics of Various CYP450 Substrates in Patients with Neuromyelitis Optica or Neuromyelitis Optica Spectrum Disorders in Different Ethnic Populations

A physiologically based pharmacokinetic (PBPK) model was used to simulate the impact of elevated levels of interleukin (IL)-6 on the exposure of several orally administered cytochrome P450 (CYP) probe substrates (caffeine, S-warfarin, omeprazole, dextromethorphan, midazolam, and simvastatin). The changes in exposure of these substrates in subjects with rheumatoid arthritis (and hence elevated IL-6 levels) compared with healthy subjects were predicted with a reasonable degree of accuracy. The PBPK model was then used to simulate the change in oral exposure of the probe substrates in North European Caucasian, Chinese, and Japanese population of patients with neuromyelitis optica (NMO) or NMO spectrum disorder with elevated plasma IL-6 levels (up to 100 pg/mL). Moderate interactions [mean AUC fold change, ≤ 2.08 (midazolam) or 2.36 (simvastatin)] was predicted for CYP3A4 probe substrates and weak interactions (mean AUC fold change, ≤ 1.29-1.97) were predicted for CYP2C19, CYP2C9, and CYP2D6 substrates. No notable interaction was predicted with CYP1A2. Although ethnic differences led to differences in simulated exposure for some of the probe substrates, there were no marked differences in the predicted magnitude of the change in exposure following IL-6-mediated suppression of CYPs. Decreased levels of serum albumin (as reported in NMO patients) had little impact on the magnitude of the simulated IL-6-mediated drug interactions. This PBPK modeling approach allowed us to leverage knowledge from different disease and ethnic populations to make predictions of cytokine-related DDIs in a rare disease population where actual clinical studies would otherwise be difficult to conduct.

Complex Drug-Drug-Gene-Disease Interactions Involving Cytochromes P450: Systematic Review of Published Case Reports and Clinical Perspectives

Drug pharmacokinetics (PK) is influenced by multiple intrinsic and extrinsic factors, among which concomitant medications are responsible for drug-drug interactions (DDIs) that may have a clinical relevance, resulting in adverse drug reactions or reduced efficacy. The addition of intrinsic factors affecting cytochromes P450 (CYPs) activity and/or expression, such as genetic polymorphisms and diseases, may potentiate the impact and clinical relevance of DDIs. In addition, greater variability in drug levels and exposures has been observed when such intrinsic factors are present in addition to concomitant medications perpetrating DDIs. This variability results in poor predictability of DDIs and potentially dramatic clinical consequences. The present review illustrates the issue of complex DDIs using systematically searched published case reports of DDIs involving genetic polymorphisms, renal impairment, cirrhosis, and/or inflammation. Current knowledge on the impact of each of these factors on drug exposure and DDIs is summarized and future perspectives for the management of such complex DDIs in clinical practice are discussed, including the use of advanced Computerized Physician Order Entry (CPOE) systems, the development of model-based dose optimization strategies, and the education of healthcare professionals with respect to personalized medicine.

Therapeutic protein-drug interactions: plausible mechanisms and assessment strategies

INTRODUCTION

Over the last three decades, therapeutic proteins have played an increasingly important role in pharmacotherapy. Owing to an expected significant increase in the coadministration of biotherapeutics with established pharmacotherapy regimens or even with other biotherapeutic agents, there is an increasing likelihood for the occurrence of clinically relevant drug interactions, so called therapeutic protein-drug interactions (TP-DIs). Areas covered: Our current understanding of TP-DIs and recent collaborations among industry, academia and regulatory agencies are reviewed in this article. Although most of the observed TP-DIs are mediated by disease states, immune status, and/or target physiology, TP-DI assessments are still done empirically. Plausible mechanisms of major TP-DIs involving therapeutic proteins (primarily monoclonal antibodies), either as victims or as perpetrators, are proposed, with mechanism-based strategies and assessment approaches to better evaluate their propensity are recommended. Expert opinion: Our current understanding of the mechanisms of TP-DIs is in its infancy. Much of the basic research needs to be conducted to verify existing TP-DI hypotheses or help predict and manage potential ones, whose efforts are not considered trivial and may be better achieved through close collaborations among scientists from academia, industry, and regulatory agencies.