Evaluation of Cytochrome P450 3A4-Mediated Drug-Drug Interaction Potential for Cobimetinib Using Physiologically Based Pharmacokinetic Modeling and Simulation

Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs

INTRODUCTION

Physiologically based pharmacokinetics (PBPK) models are increasingly used in the drug research and development, especially in anti-cancer drugs. Between 2001 and 2020, a total of 89 small-molecule targeted antitumor drugs were approved in China and the United States, some of which already included PBPK modeling in their application or approval packages. This article intended to review the prevalence and application of PBPK model in these drugs.

METHOD

Article search was performed in the PubMed to collect English research articles on small-molecule targeted anti-cancer drugs using PBPK modeling. The selected articles were classified into nine categorizes according to the application areas and further analyzed.

RESULT

From 2001 to 2020, more than 60% of small-molecule targeted anti-cancer drugs (54/89) were studied using PBPK model with a wide range of application. Ninety research articles were included, of which 48 involved enzyme-mediated drug-drug interaction (DDI). Of these retrieved articles, Simcyp, GastroPlus, and PK-Sim were the most widely model building platforms, which account for 63.8%, 15.2%, and 8.6%, respectively.

CONCLUSION

PBPK modeling is commonly and widely used to research small-molecule targeted anti-cancer drugs.

The Impact of Drug-Drug Interactions on the Toxicity Profile of Combined Treatment with BRAF and MEK Inhibitors in Patients with BRAF-Mutated Metastatic Melanoma

BACKGROUND

BRAF and MEK inhibition is a successful strategy in managing BRAF-mutant melanoma, even if the treatment-related toxicity is substantial. We analyzed the role of drug-drug interactions (DDI) on the toxicity profile of anti-BRAF/anti-MEK therapy.

METHODS

In this multicenter, observational, and retrospective study, DDIs were assessed using Drug-PIN software (V 2/23). The association between the Drug-PIN continuous score or the Drug-PIN traffic light and the occurrence of treatment-related toxicities and oncological outcomes was evaluated.

RESULTS

In total, 177 patients with advanced BRAF-mutated melanoma undergoing BRAF/MEK targeted therapy were included. All grade toxicity was registered in 79% of patients. Cardiovascular toxicities occurred in 31 patients (17.5%). Further, 94 (55.9%) patients had comorbidities requiring specific pharmacological treatments. The median Drug-PIN score significantly increased when the target combination was added to the patient's home therapy (p-value < 0.0001). Cardiovascular toxicity was significantly associated with the Drug-PIN score (p-value = 0.048). The Drug-PIN traffic light (p = 0.00821) and the Drug-PIN score (p = 0.0291) were seen to be significant predictors of cardiotoxicity. Patients with low-grade vs. high-grade interactions showed a better prognosis regarding overall survival (OS) (p = 0.0045) and progression-free survival (PFS) (p = 0.012). The survival analysis of the subgroup of patients with cardiological toxicity demonstrated that patients with low-grade vs. high-grade DDIs had better outcomes in terms of OS (p = 0.0012) and a trend toward significance in PFS (p = 0.068).

CONCLUSIONS

DDIs emerged as a critical issue for the risk of treatment-related cardiovascular toxicity. Our findings support the utility of DDI assessment in melanoma patients treated with BRAF/MEK inhibitors.

Potential drug-drug interactions with mitogen-activated protein kinase (MEK) inhibitors used to treat melanoma

INTRODUCTION

The management of patients with BRAF-mutated advanced melanoma who are undergoing targeted therapy with MEK inhibitors can be complicated by the co-administration of multiple medications, which can give rise to drug-drug interactions of clinical significance.

COVERED AREAS

Our review presents a comprehensive analysis of the pharmacokinetic and pharmacodynamic interactions of the three approved for advanced melanoma MEK inhibitor drugs - binimetinib, cobimetinib, and trametinib. MEDLINE (PubMed) was utilized for the literature search, comprising clinical studies, observational studies, and preclinical research. The review discusses the impact of these interactions on efficacy and safety of the treatments and differentiates between interactions supported by pharmacokinetic or pharmacodynamic mechanisms, those encountered in clinical practice, and those observed in preclinical studies.

EXPERT OPINION

Physicians should be aware about potential benefits, but also increased toxicity caused by drug interactions between MEK inhibitors and other drugs in the management of patients with metastatic melanoma.

Repurposing antifungal drugs for cancer therapy

BACKGROUND

Repurposing antifungal drugs in cancer therapy has attracted unprecedented attention in both preclinical and clinical research due to specific advantages, such as safety, high-cost effectiveness and time savings compared with cancer drug discovery. The surprising and encouraging efficacy of antifungal drugs in cancer therapy, mechanistically, is attributed to the overlapping targets or molecular pathways between fungal and cancer pathogenesis. Advancements in omics, informatics and analytical technology have led to the discovery of increasing "off-site" targets from antifungal drugs involved in cancerogenesis, such as smoothened (D477G) inhibition from itraconazole in basal cell carcinoma.

AIM OF REVIEW

This review illustrates several antifungal drugs repurposed for cancer therapy and reveals the underlying mechanism based on their original target and "off-site" target. Furthermore, the challenges and perspectives for the future development and clinical applications of antifungal drugs for cancer therapy are also discussed, providing a refresh understanding of drug repurposing.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review may provide a basic understanding of repurposed antifungal drugs for clinical cancer management, thereby helping antifungal drugs broaden new indications and promote clinical translation.

Involvement of Transporters in Intestinal Drug-Drug Interactions of Oral Targeted Anticancer Drugs Assessed by Changes in Drug Absorption Time

(1) Background: Oral targeted anticancer drugs are victims of presystemic pharmacokinetic drug−drug interactions (DDI). Identification of the nature of these DDIs, i.e., enzyme-based or/and transporter-based, is challenging, since most of these drugs are substrates of intestinal and/or hepatic cytochrome P-450 enzymes and of intestinal membrane transporters. (2) Methods: Variations in mean absorption time (MAT) between DDIs and control period (MAT ratios < 0.77 or >1.30) have been proposed to implicate transporters in DDIs at the intestinal level. This methodology has been applied to a large set of oral targeted anticancer drugs (n = 54, involved in 77 DDI studies), from DDI studies available either in the international literature and/or in publicly accessible FDA files. (3) Results: Significant variations in MAT were evidenced in 33 DDI studies, 12 of which could be explained by modulation of an efflux transporter. In 21 DDI studies, modulation of efflux transporters could not explain the MAT variation, suggesting a possible relevant role of influx transporters in the intestinal absorption. (4) Conclusions: This methodology allows one to suggest the involvement of intestinal transporters in DDIs, and should be used in conjunction with in vitro methodologies to help understanding the origin of DDIs.

Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling

Pemigatinib is a potent inhibitor of fibroblast growth factor receptor being developed for oncology indications. It is primarily metabolized by cytochrome P450 (CYP) 3A4, and the ratio of estimated concentration over concentration required for 50% inhibition ratio for pemigatinib as an inhibitor of P‐glycoprotein (P‐gp), organic cation transporter‐2 (OCT2), and multidrug and toxin extrusion protein‐1 (MATE1) exceeds the cutoff values established in regulatory guidance. A Simcyp minimal physiologically based pharmacokinetic (PBPK) with advanced dissolution, absorption, and metabolism absorption model for pemigatinib was developed and validated using observed clinical pharmacokinetic (PK) data and itraconazole/rifampin drug–drug interaction (DDI) data. The model accurately predicted itraconazole DDI (approximate 90% area under the plasma drug concentration–time curve [AUC] and approximate 20% maximum plasma drug concentration [C_max] increase). The model underpredicted rifampin induction by 100% (approximate 6.7‐fold decrease in AUC and approximate 2.6‐fold decrease in C_max in the DDI study), presumably reflecting non‐CYP3A4 mechanisms being impacted. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors/inducers on pemigatinib PK and pemigatinib as an inhibitor of P‐gp or OCT2/MATE1 substrates. The worst‐case scenario DDI simulation for pemigatinib as an inhibitor of P‐gp or OCT2/MATE1 substrates showed only a modest DDI effect. The recommendation based on this simulation and clinical data is to reduce pemigatinib dose for coadministration with strong and moderate CYP3A4 inhibitors. No dose adjustment is required for weak CYP3A4 inhibitors. The coadministration of strong and moderate CYP3A4 inducers with pemigatinib should be avoided. PBPK modeling suggested no dose adjustment with P‐gp or OCT2/MATE1 substrates.

Cobimetinib in Pediatric and Young Adult Patients with Relapsed or Refractory Solid Tumors (iMATRIX-cobi): A Multicenter, Phase I/II Study

Background

The MAPK pathway is an emerging target across a number of adult and pediatric tumors. Targeting the downstream effector of MAPK, MEK1, is a proposed strategy to control the growth of MAPK-dependent tumors.

Objective

iMATRIX-cobi assessed the safety, pharmacokinetics, and anti-tumor activity of cobimetinib, a highly selective MEK inhibitor, in children and young adults with relapsed/refractory solid tumors.

Patients and Methods

This multicenter Phase I/II study enrolled patients aged 6 months to < 30 years with solid tumors with known/expected MAPK pathway involvement. Patients received cobimetinib tablet or suspension formulation on Days 1–21 of a 28-day cycle. Dose escalation followed a rolling 6 design. The primary endpoint was safety; secondary endpoints were pharmacokinetics and anti-tumor activity.

Results

Of 56 enrolled patients (median age 9 years [range 3–29]), 18 received cobimetinib tablets and 38 cobimetinib suspension. Most common diagnoses were low-grade glioma (LGG; n = 32, including n = 12 in the expansion cohort) and plexiform neurofibroma within neurofibromatosis type 1 (n = 12). Six patients (11 %) experienced dose-limiting toxicities (including five ocular toxicity events), which established a pediatric recommended Phase II dose (RP2D) of 0.8 mg/kg tablet and 1.0 mg/kg suspension. Most frequently reported treatment-related adverse events were gastrointestinal and skin disorders. Steady state mean exposure (C_max, AUC_0–24) of cobimetinib at the RP2D (1.0 mg/kg suspension) was ~ 50 % lower than in adults receiving the approved 60 mg/day dose. Overall response rate was 5.4 % (3/56; all partial responses in patients with LGG).

Conclusions

The safety profile of cobimetinib in pediatrics was similar to that reported in adults. Clinical activity was observed in LGG patients with known/suspected MAPK pathway activation. Cobimetinib combination regimens may be required to improve response rates in this pediatric population.

Clinical Trial Registration

ClinicalTrials.gov NCT02639546, registered December 24, 2015.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11523-022-00888-9.

Antifungal Drugs TDM: Trends and Update

PURPOSE

The increasing burden of invasive fungal infections results in growing challenges to antifungal (AF) therapeutic drug monitoring (TDM). This review aims to provide an overview of recent advances in AF TDM.

METHODS

We conducted a PubMed search for articles during 2016-2020 using "TDM" or "pharmacokinetics" or "drug-drug-interaction" with "antifungal," consolidated for each AF. Selection was limited to English language articles with human data on drug exposure.

RESULTS

More than 1000 articles matched the search terms. We selected 566 publications. The latest findings tend to confirm previous observations in real-life clinical settings. The pharmacokinetic variability related to special populations is not specific but must be considered. AF benefit-to-risk ratio, drug-drug interaction (DDI) profiles, and minimal inhibitory concentrations for pathogens must be known to manage at-risk situations and patients. Itraconazole has replaced ketoconazole in healthy volunteers DDI studies. Physiologically based pharmacokinetic modeling is widely used to assess metabolic azole DDI. AF prophylactic use was studied more for Aspergillus spp. and Mucorales in oncohematology and solid organ transplantation than for Candida (already studied). Emergence of central nervous system infection and severe infections in immunocompetent individuals both merit special attention. TDM is more challenging for azoles than amphotericin B and echinocandins. Fewer TDM requirements exist for fluconazole and isavuconazole (ISZ); however, ISZ is frequently used in clinical situations in which TDM is recommended. Voriconazole remains the most challenging of the AF, with toxicity limiting high-dose treatments. Moreover, alternative treatments (posaconazole tablets, ISZ) are now available.

CONCLUSIONS

TDM seems to be crucial for curative and/or long-term maintenance treatment in highly variable patients. TDM poses fewer cost issues than the drugs themselves or subsequent treatment issues. The integration of clinical pharmacology into multidisciplinary management is now increasingly seen as a part of patient care.

Effects of the Moderate CYP3A4 Inhibitor Erythromycin on the Pharmacokinetics of Palbociclib: A Randomized Crossover Trial in Patients With Breast Cancer

Palbociclib is an oral inhibitor of cyclin-dependent kinases 4 and 6 used in the treatment of locally advanced and metastatic breast cancer, and is extensively metabolized by cytochrome P450 enzyme 3A4 (CYP3A4). A pharmacokinetic/pharmacodynamic relationship between palbociclib exposure and neutropenia is well known. This study aimed to investigate the effects of the moderate CYP3A4 inhibitor erythromycin on the pharmacokinetics of palbociclib. We performed a randomized crossover trial comparing the pharmacokinetics of palbociclib monotherapy 125 mg once daily (q.d.) with palbociclib 125 mg q.d. plus oral erythromycin 500 mg three times daily for seven days. Pharmacokinetic sampling was performed at steady-state for both dosing schedules. Eleven evaluable patients have been enrolled. For palbociclib monotherapy, geometric mean area under the plasma concentration-time curve from zero to infinity (AUC_0-24h ), maximum plasma concentration (C_max ), and minimum plasma concentration (C_min ) were 1.46 × 10³  ng•h/mL (coefficient of variation (CV) 45.0%), 80.5 ng/mL (CV 48.5%), and 48.4 ng/mL (CV 38.8%), respectively, compared with 2.09 × 10³  ng•h/mL (CV 49.3%, P = 0.000977), 115 ng/mL (CV 53.7%, P = 0.00562), and 70.7 ng/mL (CV 47.5%, P = 0.000488) when palbociclib was administered concomitantly with erythromycin. Geometric mean ratios (90% confidence intervals) of AUC_0-24h , C_max , and C_min for palbociclib plus erythromycin vs. palbociclib monotherapy were 1.43 (1.24-1.66), 1.43 (1.20-1.69), and 1.46 (1.30-1.63). Minor differences in adverse events were observed, and only one grade ≥ 3 toxicity was observed in this short period of time. To conclude, concomitant intake of palbociclib with the moderate CYP3A4 inhibitor erythromycin resulted in an increase in palbociclib AUC_0-24h and C_max of both 43%. Therefore, a dose reduction of palbociclib to 75 mg q.d. is rational, when palbociclib and moderate CYP3A4 inhibitors are used concomitantly.

A Review of CYP3A Drug-Drug Interaction Studies: Practical Guidelines for Patients Using Targeted Oral Anticancer Drugs

Many oral anticancer drugs are metabolized by CYP3A. Clinical drug-drug interaction (DDI) studies often only examine the effect of strong CYP3A inhibitors and inducers. The effect of moderate or weak inhibitors or inducers can be examined using physiologically based pharmacokinetic simulations, but data from these simulations are not always available early after approval of a drug. In this review we provide recommendations for clinical practice on how to deal with DDIs of oral anticancer drugs if only data from strong CYP3A inhibitors or inducers is available. These recommendations were based on reviewed data of oral anticancer drugs primarily metabolized by CYP3A and approved for the treatment of solid tumors from January 1st, 2013 to December 31st, 2015. In addition, three drugs that were registered before the new EMA guideline was issued (i.e., everolimus, imatinib, and sunitinib), were reviewed. DDIs are often complex, but if no data is available from moderate CYP3A inhibitors/inducers, a change in exposure of 50% compared with strong inhibitors/inducers can be assumed. No a priori dose adaptations are indicated for weak inhibitors/inducers, because their interacting effect is small. In case pharmacologically active metabolites are involved, the metabolic pathway, the ratio of the parent to the metabolites, and the potency of the metabolites should be taken into account.

Drug-Drug Interaction Between Oxycodone and Diazepam by a Combined in Silico Pharmacokinetic and Pharmacodynamic Modeling Approach

Opioids and benzodiazepines have complex drug-drug interactions (DDIs), which serve as an important source of adverse drug effects. In this work, we predicted the DDI between oxycodone (OXY) and diazepam (DZP) in the human body by applying in silico pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation. First, we studied the PK interaction between OXY and DZP with a physiologically based pharmacokinetic (PBPK) model. Second, we applied molecular modeling techniques including molecular docking, molecular dynamics (MD) simulation, and the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) free energy method to predict the PD-DDI between these two drugs. The PK interaction between OXY and DZP predicted by the PBPK model was not obvious. No significant interaction was observed between the two drugs at normal doses, though very high doses of DZP demonstrated a non-negligible inhibitory effect on OXY metabolism. On the contrary, the molecular modeling study shows that DZP has potential to compete with OXY at the same binding pocket of the active μ-opioid receptor (MOR) and κ-opioid receptor (KOR). MD simulation and MM-PBSA calculation results demonstrated that there is likely a synergetic effect between OXY and DZP binding to opioid receptors, as OXY is likely to target the active MOR while DZP selectively binds to the active KOR. Thus, pharmacokinetics contributes slightly to the DDI between OXY and DZP although an overdose of DZP has been brought to attention. Pharmacodynamics is likely to play a more important role than pharmacokinetics in revealing the mechanism of DDI between OXY and DZP.

Physiologically based pharmacokinetic modeling of altered tizanidine systemic exposure by CYP1A2 modulation: Impact of drug-drug interactions and cigarette consumption

Tizanidine is an alpha2-adrenergic agonist, used to treat spasticity associated with multiple sclerosis and spinal injury. Tizanidine is primarily metabolized by CYP1A2 and is considered a sensitive index substrate for this enzyme. The physiologically based pharmacokinetic (PBPK) modeling platform Simcyp® was used to evaluate the impact of CYP1A2 modulation on tizanidine exposure through drug-drug interactions (DDIs) and host-dependent habits (cigarette smoking). A PBPK model was developed to predict tizanidine disposition in healthy volunteers following oral administration. The model was verified based on agreement between model-simulated and clinically observed systemic exposure metrics (C_max, AUC). The model was then used to carry-out DDI simulations to predict alterations in tizanidine systemic exposure when co-administered with various CYP1A2 perpetrators including competitive inhibitors (fluvoxamine, ciprofloxacin), a mechanism-based inhibitor (rofecoxib), and an inducer (rifampin). Additional simulations were performed to evaluate the impact of cigarette smoking on systemic exposure. Under each scenario, the PBPK model was able to capture the observed fold changes in tizanidine C_max and AUC of tizanidine when coadministered with CYP1A2 inhibitors or inducers. These results add to the available research findings in the literature on PBPK predictions of drug-drug interactions and illustrate the potential application in drug development, specifically to support product labeling.

Inhibition and induction of CYP enzymes in humans: an update

The cytochrome P450 (CYP) enzyme family is the most important enzyme system catalyzing the phase 1 metabolism of pharmaceuticals and other xenobiotics such as herbal remedies and toxic compounds in the environment. The inhibition and induction of CYPs are major mechanisms causing pharmacokinetic drug–drug interactions. This review presents a comprehensive update on the inhibitors and inducers of the specific CYP enzymes in humans. The focus is on the more recent human in vitro and in vivo findings since the publication of our previous review on this topic in 2008. In addition to the general presentation of inhibitory drugs and inducers of human CYP enzymes by drugs, herbal remedies, and toxic compounds, an in-depth view on tyrosine-kinase inhibitors and antiretroviral HIV medications as victims and perpetrators of drug–drug interactions is provided as examples of the current trends in the field. Also, a concise overview of the mechanisms of CYP induction is presented to aid the understanding of the induction phenomena.

Effect of Hepatic Impairment on Cobimetinib Pharmacokinetics: The Complex Interplay Between Physiological Changes and Drug Characteristics

Cobimetinib is a kinase inhibitor indicated for use in combination with vemurafenib for treatment of unresectable/metastatic melanoma with specific BRAF mutations. Cobimetinib is extensively metabolized in liver; thus, patients with hepatic impairment (HI) might have increased cobimetinib exposure. In this study, we investigated the impact of HI on the pharmacokinetics (PK) and safety of cobimetinib. Subjects with normal hepatic function and mild to severe HI were enrolled. All subjects received a single oral dose of 10 mg cobimetinib, and serial blood samples were collected at specified times. Cobimetinib PK in subjects with mild and moderate HI was similar to that in those with normal liver function. However, subjects with severe HI, on average, showed ∼30% lower total AUC_0-∞ and ∼2-fold higher unbound AUC_0-∞ compared with those with normal hepatic function. These exposure differences can be explained by lower albumin levels observed in subjects with severe HI, the strong correlation between albumin level and the unbound fraction and the general PK variability of cobimetinib. In addition, previous studies with cobimetinib showed a lack of an exposure-response relationship for efficacy and safety. Therefore, collectively, our results suggest that the starting dose for patients with hepatic impairment can be the same as that for those with normal hepatic function.

Utilization of Physiologically Based Pharmacokinetic Modeling in Clinical Pharmacology and Therapeutics: an Overview

The purpose of this review was to assess the advancement of applications for physiologically based pharmacokinetic (PBPK) modeling in various therapeutic areas. We conducted a PubMed search, and 166 articles published between 2012 and 2018 on FDA-approved drug products were selected for further review. Qualifying publications were summarized according to therapeutic area, medication(s) studied, pharmacokinetic model type utilized, simulator program used, and the applications of that modeling. The results showed a 13-fold increase in the number of papers published from 2012 to 2018, with the largest proportion of articles dedicated to the areas of infectious diseases, oncology, and neurology, and application extensions including prediction of drug-drug interactions due to metabolism and/or transporter-mediated effects and understanding drug kinetics in special populations. In addition, we profiled several high-impact studies whose results were used to guide package insert information and formulate dose recommendations. These results show that while utilization of PBPK modeling has drastically increased over the past several years, regulatory support, lack of easy-to-use systems for clinicians, and challenges with model validation remain major challenges for the widespread adoption of this practice in institutional and ambulatory settings. However, PBPK modeling will continue to be a useful tool in the future to assess therapeutic drug monitoring and the growing field of personalized medicine.

Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution

In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fu_p) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.

Interaction between phytotherapy and oral anticancer agents: prospective study and literature review

Cancer is becoming more prevalent in elderly patient. Due to polypharmacy, older adults with cancer are predisposed to drug-drug interactions. There is also an increasing interest in the use of complementary and alternative medicine (CAM). Thirty to seventy percent of patients with cancer have used CAM. Through pharmaceutical counseling sessions, we can provide advices on herb-drug interactions (HDI). All the patients seen in pharmaceutical counseling sessions were prospectively included. Information was collected during these sessions: prescribed medication (oral anticancer agents (OAA) and other drugs), CAM (phytotherapy especially), and use of over-the-counter (OTC) drugs. If pharmacist considered an interaction or an intervention clinically relevant, the oncologist was notified. Then, a literature review was realized to identify the potential HDI (no interactions, precautions for use, contraindication). Among 201 pharmacist counseling sessions, it resulted in 104 interventions related to 46 HDI, 28 drug-drug interactions and 30 others (wrong dosage, omission…). To determine HDI, we review 73 medicinal plants which are used by our patients with cancer and 31 OAA. A total of 1829 recommendations were formulated about 59 (75%) medical plants and their interaction with an OAA. Herb-drug interactions should not be ignored by healthcare providers in their management of cancer patients in daily practice.

Improved predictions of time-dependent drug-drug interactions by determination of cytosolic drug concentrations

The clinical impact of drug-drug interactions based on time-dependent inhibition of cytochrome P450 (CYP) 3A4 has often been overpredicted, likely due to use of improper inhibitor concentration estimates at the enzyme. Here, we investigated if use of cytosolic unbound inhibitor concentrations could improve predictions of time-dependent drug-drug interactions. First, we assessed the inhibitory effects of ten time-dependent CYP3A inhibitors on midazolam 1′-hydroxylation in human liver microsomes. Then, using a novel method, we determined the cytosolic bioavailability of the inhibitors in human hepatocytes, and used the obtained values to calculate their concentrations at the active site of the enzyme, i.e. the cytosolic unbound concentrations. Finally, we combined the data in mechanistic static predictions, by considering different combinations of inhibitor concentrations in intestine and liver, including hepatic concentrations corrected for cytosolic bioavailability. The results were then compared to clinical data. Compared to no correction, correction for cytosolic bioavailability resulted in higher accuracy and precision, generally in line with those obtained by more demanding modelling. The best predictions were obtained when the inhibition of hepatic CYP3A was based on unbound maximal inhibitor concentrations corrected for cytosolic bioavailability. Our findings suggest that cytosolic unbound inhibitor concentrations improves predictions of time-dependent drug-drug interactions for CYP3A.

Oral oncolytic and antiretroviral therapy administration: dose adjustments, drug interactions, and other considerations for clinical use

The rise in non-AIDS defining cancers (NADCs) is emerging as a leading cause of death for HIV and cancer patients. To address this, current literature and guidelines suggest the continuation of antiretroviral therapy (ART) with oral oncolytic agents to prevent adverse complications associated with HIV disease progression. However, such an approach has the potential for drug-drug interactions and adverse events for patients on such therapy. Further, recommendations on how to adjust these medications, when used concomitantly, are limited. As such, our purpose is to evaluate existing literature through such means as drug databases (e.g. Micromedex, Lexi-Comp, etc.) and package inserts along with PubMed/Medline, Embase, and Google Scholar databases to develop a reference tool for providers to utilize when there is a decision to treat a patient with ART and oral oncolytic agents concurrently. Our findings suggest that there are many drug interactions that should be taken into consideration with dual therapy. Metabolism is a key determinant of dose adjustment, and many oncolytic agents and ART agents must have their dose adjusted as such. Most notably, several tyrosine kinase inhibitors require dose increases when used with non-nucleoside reverse transcriptase inhibitors (NNRTIs) but must be decreased when used concomitantly with protease inhibitors (PIs) and cobicistat. Further findings suggest that certain agents should not be used together, which include, but are not limited to, such combinations as bosutinib with NNRTIs, cobicistat, or PIs; idelalisib with maraviroc or PIs; neratinib with NNRTIs, cobicistat, or PIs; and venetoclax with NNRTIs. Overall, the most prominent oncolytic drug interactions were discovered when such agents were used concomitantly with PIs, cobicistat-boosted elvitegravir, or NNRTIs. Future studies are necessary to further evaluate the use of these agents together in disease therapy to generate absolute evidence of such findings. However, from the studies evaluated, much evidence exists to suggest that concomitant therapy is not without drug interactions. As such, clinical decisions regarding concomitant therapy should be evaluated in which the risk and benefit of dual therapy are assessed. Dose adjustments must be made accordingly and in consultation with both HIV and oncology clinicians and pharmacists to reduce the risk for adverse outcomes and disease progression for those with cancer and HIV/AIDS.

Clinically relevant drug interactions with multikinase inhibitors: a review

Multikinase inhibitors (MKIs), including the tyrosine kinase inhibitors (TKIs), have rapidly become an established factor in daily (hemato)-oncology practice. Although the oral route of administration offers improved flexibility and convenience for the patient, challenges arise in the use of MKIs. As MKIs are prescribed extensively, patients are at increased risk for (severe) drug–drug interactions (DDIs). As a result of these DDIs, plasma pharmacokinetics of MKIs may vary significantly, thereby leading to high interpatient variability and subsequent risk for increased toxicity or a diminished therapeutic outcome. Most clinically relevant DDIs with MKIs concern altered absorption and metabolism. The absorption of MKIs may be decreased by concomitant use of gastric acid-suppressive agents (e.g. proton pump inhibitors) as many kinase inhibitors show pH-dependent solubility. In addition, DDIs concerning drug (uptake and efflux) transporters may be of significant clinical relevance during MKI therapy. Furthermore, since many MKIs are substrates for cytochrome P450 isoenzymes (CYPs), induction or inhibition with strong CYP inhibitors or inducers may lead to significant alterations in MKI exposure. In conclusion, DDIs are of major concern during MKI therapy and need to be monitored closely in clinical practice. Based on the current knowledge and available literature, practical recommendations for management of these DDIs in clinical practice are presented in this review.

Cobimetinib (GDC-0973, XL518)

The mitogen-activated protein kinase cascade (MAPK/ERK pathway) is a signaling pathway activated as a cellular response to various stimuli and for regulating the proliferation and survival of several types of eukaryotic cells, among others a wide variety of tumor cells. Mutations of the proteins involved in this pathway have been discovered in several tumor entities, indicating their inhibition as a potential therapeutic target. BRAF inhibitors have been in the clinical use since 2011. Several MEK inhibitors have been studied for metastatic cancer treatment in the recent past. After trametinib, cobimetinib is another potent, selective oral MEK1/2 inhibitor that was approved by European Medicine Agency (EMA) and Food and Drug Administration (FDA) in 2015 for treatment of malignant melanoma in a combination with the BRAF inhibitor vemurafenib.

Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review

PURPOSE

Physiologically based pharmacokinetic (PBPK) modeling, a mathematical modeling approach which uses a pharmacokinetic model to mimick human physiology to predict drug concentration-time profiles, has been used for the discover and development of drugs in various fields, including oncology, since 2000. There have been a few general review articles on the utilization of PBPK in the development of oncology drugs, but these do not include an evaluation of model prediction accuracy. We therefore conducted a systematic review to define the accuracy of PBPK model prediction and its utility throughout all the developmental phases of oncology drugs.

METHODS

A systematic search was performed in the PubMed, PubMed Central and Cochrane Library databases from 1980 to February 2017 for articles (1) written in English, (2) focused on oncology or antineoplastic or anticancer drugs, tumor or cancer or anticancer drugs listed in the U.S. National Institutes of Health and (3) involving a PBPK model. The absolute-average-folding-errors (AAFEs) of the area under the curve (AUC) between predicted and observed values in each article were calculated to assess model prediction accuracy.

RESULTS

Of the 2341 articles initially identified by our search of the databases, 40 were included in the review analysis. These articles reported on six types of studies, i.e. in vivo (n = 4), first-in-human (n = 5), phase II/III clinical trials (n = 9), organ impairment (n = 3), pediatrics (n = 4) and drug-drug interactions (n = 15). AAFEs of the predicted AUC for all groups of studies were within 1.3-fold of each other despite variations in experimental methodologies.

CONCLUSION

PBPK modeling is a potential tool which can be effectively applied throughout all phases of oncology drug development. The number of experimental animals and human participants enrolled in the studies can be reduced using PBPK modeling and PBPK-population-PK modeling. The limited number of publications of unsuccessful model application to date may contribute to bias toward the usefulness of modeling.

Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective

This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.

An UPLC-MS/MS method for the quantification of BRAF inhibitors (vemurafenib, dabrafenib) and MEK inhibitors (cobimetinib, trametinib, binimetinib) in human plasma. Application to treated melanoma patients

Targeted therapies for cancers are fast-growing therapies. For instance kinase inhibitors such as BRAF inhibitors (BRAFi) and MEK inhibitors (MEKi) are increasingly used to treat malignant melanoma. The metabolic profile of these drugs can result in great interindividual variability, justifying therapeutic drug monitoring (TDM). We describe a rapid and specific method for quantification of 2 BRAFi (vemurafenib, dabrafenib) and 3 MEKi (cobimetinib, trametinib and binimetinib). Chromatography was performed on a Waters Acquity-UPLC system with CORTECS C18+ column, under a gradient of 10% acetic acid in water/acetonitrile. An Acquity-TQD® with electrospray ionization was used for detection. Samples were prepared by solid phase extraction (Oasis® MCX microElution) before injection in the system. Calibration curves ranges from 0.4 to 100μg/ml for vemurafenib, from 1 to 1000ng/ml for dabrafenib, from 0.5 to 500ng/ml for cobimetinib and binimetinib, and from 0.75 to 250ng/ml for trametinib. At all concentrations the bias was within ±15% of the nominal concentrations and precision was ≤15%. All results were within the acceptance criteria of the EMA guidelines on method validation. This rapid, sensitive and specific UPLC-MS/MS method can perform simultaneous quantification of targeted therapies used in malignant melanoma and is usable for routine TDM.

Safety of direct antiviral agents for treatment of hepatitis C virus infection

Antiviral drugs for treatment of hepatitis C have grown dramatically in recent years. The emergence of direct antiviral agents (DAAs) is a major advance in hepatitis C treatment. Since the first generation DAAs appeared in 2011, two more generations of DAAs have been approved by the Food and Drug Administration in only five years. With the advantages of coverage of all genotypes, high availability and easy administration, DAAs have been widely used worldwide in recent years. DAAs have nearly solved the main problems encountered in hepatitis C antiviral therapy. However, adverse events associated with DAAs use have been reported constantly, and the safety of DAAs has attracted more and more attention. According to recent reports, the adverse reactions of DAAs have been gradually reduced from the first generation to the third generation. When combined with other drugs, the drug-drug interactions (DDIs) need special attention. In addition, the usage of DAAs in special patient groups should be cautious. In this paper, we will summarize the adverse effects and DDIs of DAAs as well as their usage in the general population and special patient groups.