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

Cytokine Dynamics in Action: A Mechanistic Approach to Assess Interleukin 6 Related Therapeutic Protein-Drug-Disease Interactions

Understanding cytokine-related therapeutic protein-drug interactions (TP-DI) is crucial for effective medication management in conditions characterized by elevated inflammatory responses. Recent FDA and ICH guidelines highlight a systematic, risk-based approach for evaluating these interactions, emphasizing the need for a thorough mechanistic understanding of TP-DIs. This study integrates the physiologically based pharmacokinetic (PBPK) model for TP (specifically interleukin-6, IL-6) with small-molecule drug PBPK models to elucidate cytokine-related TP-DI mechanistically. The integrated model successfully predicted TP-DIs across a broad range of both constant and fluctuating IL-6 levels, as observed in patients with rheumatoid arthritis, Crohn's disease, HIV-infection, and those undergoing hip-surgery or bone marrow transplantation (all simulated AUC and Cmax ratios were within a twofold error of the observed data). Constant IL-6 levels that would be associated with mild, moderate, and strong inhibitory interactions were estimated. The time-course and extent of TP-DI potential were also assessed in cytokine storm triggered by SARS-CoV-2 infection (COVID-19) and T-cell engager therapies (blinatumomab, mosunetuzumab, and epcoritamab). Additionally, scenarios involving concurrent CYP enzyme suppression by IL-6 and induction by rifampicin were assessed for the magnitude of drug interaction. By providing a robust mechanistic framework for understanding cytokine-drug interactions and establishing reliable exposure-response relationships, this study enhances predictive accuracy and informs human dosing strategies. It demonstrates the potential of PBPK models to improve therapeutic decision making and patient care, particularly in inflammatory conditions.

Physiologically-based pharmacokinetic modeling to predict the exposure and provide dosage regimens of tacrolimus in pregnant women with infection disease

Tacrolimus is extensively used for the prevention of graft rejection following solid organ transplantation in pregnant women. However, knowledge gaps in the dosage of tacrolimus for pregnant patients with different CYP3A5 genotypes and infection conditions have been identified. This study aimed to develop a pregnant physiologically based pharmacokinetic (PBPK) model to characterize the maternal and fetal pharmacokinetics of tacrolimus during pregnancy and explore and provide dosage adjustments. We developed PBPK models for nonpregnant patients and validated them via data from previous clinical studies using PK-Sim and Mobi software. To extrapolate to pregnancy, we considered anatomical, physiological, and metabolic alterations and simulated tacrolimus by adding six groups of IL-6 concentrations (0, 5, 25, 50, 500, and 5000 pg/mL). Models were verified by assessing goodness-of-fit plots and ratios of predicted-to-observed pharmacokinetic parameters. The developed PBPK models adequately describe the available clinical data; the fold errors of the predicted and observed values of the area under the curve and peak plasma concentration were between 0.59 and 1.64, and the average folding error and the absolute average folding error values for all concentration-time data points were 1.15 and 1.36, respectively. The simulation results indicated that the area under the steady-state concentration‒time curve and trough concentrations decreased from the first to the third trimester of pregnancy. The trough concentrations were not within the therapeutic range (4-11 ng/mL) in pregnant patients with the CYP3A5 genotype for most of the infection conditions and exceeded its effective concentration in all the CYP3A5 nonexpressers. Based on the model-derived dosing regimen, the tacrolimus trough concentration in pregnant patients with different CYP3A5 genotypes could fall into the therapeutic window, which provided a clinical practice reference for dosage adjustments during pregnancy.

The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases

Autoimmune diseases (AIDs) are a group of disorders in which the immune system attacks the body's own tissues, leading to chronic inflammation and organ damage. These diseases are difficult to treat due to variability in drug PK among individuals, patient responses to treatment, and the side effects of long-term immunosuppressive therapies. In recent years, pharmacometrics has emerged as a critical tool in drug discovery and development (DDD) and precision medicine. The aim of this review is to explore the diverse roles that pharmacometrics has played in addressing the challenges associated with DDD and personalized therapies in the treatment of AIDs. Methods: This review synthesizes research from the past two decades on pharmacometric methodologies, including Physiologically Based Pharmacokinetic (PBPK) modeling, Pharmacokinetic/Pharmacodynamic (PK/PD) modeling, disease progression (DisP) modeling, population modeling, model-based meta-analysis (MBMA), and Quantitative Systems Pharmacology (QSP). The incorporation of artificial intelligence (AI) and machine learning (ML) into pharmacometrics is also discussed. Results: Pharmacometrics has demonstrated significant potential in optimizing dosing regimens, improving drug safety, and predicting patient-specific responses in AIDs. PBPK and PK/PD models have been instrumental in personalizing treatments, while DisP and QSP models provide insights into disease evolution and pathophysiological mechanisms in AIDs. AI/ML implementation has further enhanced the precision of these models. Conclusions: Pharmacometrics plays a crucial role in bridging pre-clinical findings and clinical applications, driving more personalized and effective treatments for AIDs. Its integration into DDD and translational science, in combination with AI and ML algorithms, holds promise for advancing therapeutic strategies and improving autoimmune patients' outcomes.

Evaluation of Drug-Drug Interaction Potential of Talquetamab, a T-Cell-Redirecting GPRC5D × CD3 Bispecific Antibody, as a Result of Cytokine Release Syndrome in Patients with Relapsed/Refractory Multiple Myeloma in MonumenTAL-1, Using a Physiologically Based Pharmacokinetic Model

BACKGROUND

Cytokine release syndrome, commonly associated with T-cell immunotherapies, was observed with talquetamab, a T-cell-redirecting bispecific antibody, in the phase I/II MonumenTAL-1 study, leading to elevated interleukin (IL)-6, which can suppress cytochrome P450 (CYP) enzyme activity.

OBJECTIVE

We aimed to evaluate the potential impact of elevated IL-6 on the exposure of co-administered CYP450 substrates for two scenarios: (1) the observed median IL-6 profile and (2) a profile with the highest IL-6 maximum concentration following talquetamab treatment.

METHODS

A physiologically based pharmacokinetic model was developed based on the literature and simulations performed using observed IL-6 profiles from patients in MonumenTAL-1 who received the subcutaneous recommended phase 2 doses (RP2Ds) of talquetamab: 0.4 mg/kg weekly (QW) and 0.8 mg/kg every other week (Q2W).

RESULTS

Median IL-6 maximum concentration was 18.4 and 7.1 pg/mL, and maximum IL-6 maximum concentration was 213 and 3503 pg/mL for talquetamab QW and Q2W RP2Ds, respectively. For the median IL-6 profile, no interaction between IL-6 and studied CYP substrates was predicted at either RP2D. The maximum IL-6 profile predicted weak-to-moderate impact on exposure of CYP2C19, CYP3A4, and CYP3A5 substrates and minimal impact on exposure of CYP1A2 substrates at both RP2Ds. Impact on exposure of CYP2C9 substrates was predicted as minimal at QW and minimal-to-weak at Q2W RP2Ds. Time to return to 20% difference from baseline enzymatic activity was predicted as 7 and 9 days after start of cycle 1 for QW and Q2W RP2Ds, respectively.

CONCLUSIONS

These modeling results suggest that IL-6 release due to talquetamab-induced cytokine release syndrome has limited impact on potential drug-drug interactions, with the highest likelihood of impact occurring from initiation of talquetamab step-up dosing up to 7 (QW) or 9 (Q2W) days after first treatment dose in cycle 1 and during and after cytokine release syndrome. Multiple myeloma can be treated with immunotherapies such as the bispecific antibody, talquetamab, which binds the novel antigen G protein-coupled receptor family C group 5 member D on multiple myeloma cells and CD3 on T cells and induces T-cell-mediated lysis of multiple myeloma cells. Following talquetamab treatment, many patients experience cytokine release syndrome, an inflammatory immune response where levels of proinflammatory cytokines, including interleukin (IL)-6, are increased. Interleukin-6 can suppress the activity of important enzymes in the body (cytochrome [CYP] P450s) that are involved in drug clearance. This study used a physiologically based pharmacokinetic computer model to investigate the potential impact of increased IL-6 levels on CYP450 enzymes to determine subsequent impact on drugs that are metabolized by CYP450 enzymes. The results showed no predicted interaction between median levels of IL-6 observed in patients and CYP substrates (such as caffeine and omeprazole) with talquetamab. In a simulation that assessed higher (maximum) IL-6 levels observed in patients, the predicted impact of IL-6 was minimal to weak for most of the CYP substrates assessed. The effect on CYP450 enzymatic activity was highest from initiation of talquetamab step-up dosing up to 7-9 days after the first treatment dose of talquetamab. These results suggest that, in this treatment time period, elevated IL-6 levels due to talquetamab-induced cytokine release syndrome have limited impact on drugs that are CYP substrates that may be used in conjunction with talquetamab, but that the concentration and toxicity of these drugs should be monitored and the dose of CYP substrate adjusted as required.

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.

Altered Bioavailability and Pharmacokinetics in Crohn's Disease: Capturing Systems Parameters for PBPK to Assist with Predicting the Fate of Orally Administered Drugs

Backgrond and Objective

Crohn’s disease (CD) is a chronic inflammatory bowel disease that affects a wide age range. Hence, CD patients receive a variety of drugs over their life beyond those used for CD itself. The changes to the integrity of the intestine and its drug metabolising enzymes and transporters (DMETs) can alter the oral bioavailability of drugs. However, there are other changes in systems parameters determining the fate of drugs in CD, and understanding these is essential for dose adjustment in patients with CD.

Methods

The current analysis gathered all the available clinical data on the kinetics of drugs in CD (by March 2021), focusing on orally administered small molecule drugs. A meta-analysis of the systems parameters affecting oral drug pharmacokinetics was conducted. The systems information gathered on intestine, liver and blood proteins and other physiological parameters was incorporated into a physiologically based pharmacokinetic (PBPK) platform to create a virtual population of CD patients, with a view for guiding dose adjustment in the absence of clinical data in CD.

Results

There were no uniform trends in the reported changes in reported oral bioavailability. The nature of the drug as well as the formulation affected the direction and magnitude of variation in kinetics in CD patients relative to healthy volunteers. Even for the same drug, the reported changes in exposure varied, possibly due to a lack of distinction between the activity states of CD. The highest alteration was seen with S-verapamil and midazolam, 8.7- and 5.3-fold greater exposure, respectively, in active CD patients relative to healthy volunteers. Only one report was available on liver DMETs in CD, and indicated reduced CYP3A4 activity. In a number of reports, mRNA expression of DMETs in the ileum and colon of CD patients was measured, focussing on P-glycoprotein (p-gp) transporter and CYP3A4 enzyme, and showed contradictory results. No data were available on protein expression in duodenum and jejunum despite their dominant role in oral drug absorption.

Conclusion

There are currently inadequate dedicated clinical or quantitative proteomic studies in CD to enable predictive PBPK models with high confidence and adequate verification. The PBPK models for CD with the available systems parameters were able to capture the major physiological influencers and the gaps to be filled by future research. Quantification of DMETs in the intestine and the liver in CD is warranted, alongside well-defined clinical drug disposition studies with a number of index drugs as biomarkers of changes in DMETs in these patients, to avoid large-scale dedicated studies for every drug to determine the effects of disease on the drug’s metabolism and disposition and the consequential safety and therapeutic concerns.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-022-01169-4.

Tackling Inflammatory Bowel Diseases: Targeting Proinflammatory Cytokines and Lymphocyte Homing

Inflammatory bowel diseases (IBDs) are characterized by chronic inflammatory disorders that are a result of an abnormal immune response mediated by a cytokine storm and immune cell infiltration. Proinflammatory cytokine therapeutic agents, represented by TNF inhibitors, have developed rapidly over recent years and are promising options for treating IBD. Antagonizing interleukins, interferons, and Janus kinases have demonstrated their respective advantages in clinical trials and are candidates for anti-TNF therapeutic failure. Furthermore, the blockade of lymphocyte homing contributes to the excessive immune response in colitis and ameliorates inflammation and tissue damage. Factors such as integrins, selectins, and chemokines jointly coordinate the accumulation of immune cells in inflammatory regions. This review assembles the major targets and agents currently targeting proinflammatory cytokines and lymphatic trafficking to facilitate subsequent drug development.