Changes In Drug Exposure Research Articles

Comparative Analysis of Traditional and Pharmacometric-Based Pharmacoeconomic Modeling in the Cost-Utility Evaluation of Sunitinib Therapy.

Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST). A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario. The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16). Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.

Simplified processing and rapid quantification of buprenorphine, norbuprenorphine, and their conjugated metabolites in human plasma using UPLC-MS/MS: Assessment of buprenorphine exposure during opioid use disorder treatment.

Opioid use disorder (OUD) is a chronic neurobehavioral ailment and is prevalent in pregnancy. OUD is commonly treated with methadone or buprenorphine (BUP). Pregnancy is known to alter the pharmacokinetics of drugs and may lead to changes in drug exposure and response. A simple, specific, and sensitive analytical method for measuring the parent drug and its metabolites is valuable for assessing the impact of pregnancy on drug exposure. A new liquid chromatography-tandem mass spectrometric method that utilized a simple protein precipitation procedure for sample preparation and four deuterated internal standards for quantification was developed and validated for BUP and its major metabolites (norbuprenorphine [NBUP], buprenorphine-glucuronide [BUP-G], and norbuprenorphine-glucuronide [NBUP-G]) in human plasma. The standard curve was linear over the concentration range of 0.05-100 ng/mL for BUP and NBUP, and 0.1-200 ng/mL for BUP-G and NBUP-G. Intra- and inter-day bias and precision were within ±15% of nominal values for all the analytes. Quality controls assessed at four levels showed high recovery consistently for all the analytes with minimal matrix effect. Adequate analyte stability was observed at various laboratory conditions tested. Overall, the developed method is simple, sensitive, accurate and reproducible, and was successfully applied for the quantification of BUP and its metabolites in plasma samples collected from pregnant women in a clinical study assessing BUP exposure during OUD treatment.

Utility of PBPK Modeling in Predicting and Characterizing Clinical Drug Interactions.

Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions. With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late stage drug development and is often utilized to support regulatory packages for new drug applications. This minireview will briefly describe the approaches to predicting DDI utilizing PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. Significance Statement PBPK models have been a key tool in the characterization of various pharmacokinetic phenomenon, including drug-drug interactions. This minireview will highlight recent advancements and publications around PBPK DDI modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

Transcriptional adaptation of Mycobacterium tuberculosis that survives prolonged multi-drug treatment in mice.

A major reason that curing tuberculosis requires prolonged treatment is that drug exposure changes bacterial phenotypes. The physiologic adaptations of Mycobacterium tuberculosis that survive drug exposure in vivo have been obscure due to low sensitivity of existing methods in drug-treated animals. Using the novel SEARCH-TB RNA-seq platform, we elucidated Mycobacterium tuberculosis phenotypes in mice treated for with the global standard 4-drug regimen and compared them with the effect of the same regimen in vitro. This first view of the transcriptome of the minority Mycobacterium tuberculosis population that withstands treatment in vivo reveals adaptation of a broad range of cellular processes, including a shift in metabolism and cell wall modification. Surprisingly, the change in gene expression induced by treatment in vivo and in vitro was largely similar. This apparent "portability" from in vitro to the mouse provides important new context for in vitro transcriptional analyses that may support early preclinical drug evaluation.

Renal Pharmacokinetic Adaptation to Cholestasis Causes Increased Nephrotoxic Drug Accumulation by Mrp6 Downregulation in Mice

In hepatic dysfunction, renal pharmacokinetic adaptation can be observed, although information on the changes in drug exposure and the interorgan regulation of membrane transporters in kidney in liver diseases is limited. This study aimed to clarify the effects of renal exposure to nephrotoxic drugs during cholestasis induced by bile duct ligation (BDL). Among the 11 nephrotoxic drugs examined, the tissue accumulation of imatinib and cisplatin in kidney slices obtained from mice 2 weeks after BDL operation was higher than that in sham-operated mice. The uptake of imatinib in the kidney slices of BDL mice was slightly higher, whereas its efflux from the slices was largely decreased compared to that in sham-operated mice. Proteomic analysis revealed a reduction in renal expression of the efflux transporter multidrug resistance-associated protein 6 (Mrp6/Abcc6) in BDL mice, and both imatinib and cisplatin were identified as Mrp6 substrates. Survival probability after cisplatin administration was reduced in BDL mice. In conclusion, the present study demonstrated that BDL-induced cholestasis leads to the downregulation of the renal basolateral efflux transporter Mrp6, resulting in drug accumulation in renal cells and promoting drug-induced renal injury.

Dose Adjustment in Patients with Liver Cirrhosis - Comparison of Two Different Modeling Approaches.

Failure to perform adequate dose adjustment in patients with liver cirrhosis may be associated with increased toxicity. We compared the prediction of area under the curve (AUC) and clearance for the six compounds of the Basel phenotyping cocktail (caffeine, efavirenz, flurbiprofen, omeprazole, metoprolol, and midazolam) using a well-known physiology-based pharmacokinetic approach (physiologically-based pharmacokinetic [PBPK] approach, Simcyp) and a novel top-down method based on the systemic clearance in healthy volunteers adjusted for markers of liver and renal dysfunction ("top-down approach"). With few exceptions, plasma concentration-time curves were accurately predicted by the PBPK approach. In comparison to the measured AUC and clearance of these drugs in patients with liver cirrhosis and healthy controls, except for efavirenz, the estimates of both approaches were within two standard deviations of the mean for total and free drug concentrations. For both approaches, a correction factor for dose adjustment in patients with liver cirrhosis could be calculated for the drugs administered. AUCs calculated using the adjusted doses were comparable to the AUCs measured in control subjects, with slightly more accurate predictions generated by the PBPK approach. For drugs with a free fraction < 50%, predictions using free drug concentrations were more accurate than with total drug concentrations. In conclusion, both methods provided good qualitative predictions of the changes by liver cirrhosis in the pharmacokinetics of the six compounds investigated. The top-down approach is easier to implement but the PBPK approach predicted changes in drug exposure more accurately than the top-down approach and provided reliable estimates for plasma concentrations.

Evaluating the performance of machine-learning regression models for pharmacokinetic drug-drug interactions.

Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug-drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine-learning models have been developed that can classify drug-drug interactions. However, to enable quantification of the pharmacokinetic effects of a drug-drug interaction, regression-based machine learning should be explored. Therefore, this study investigated the use of regression-based machine learning to predict changes in drug exposure caused by pharmacokinetic drug-drug interactions. Fold changes in exposure relative to substrate drug monotherapy were collected from 120 clinical drug-drug interaction studies extracted from the Washington Drug Interaction Database and SimCYP compound library files. Drug characteristics (features) were collected such as structure, physicochemical properties, in vitro pharmacokinetic properties, cytochrome P450 metabolic activity, and population characteristics. Three different regression-based supervised machine-learning models were then applied to the prediction task: random forest, elastic net, and support vector regressor. Model performance was evaluated using fivefold cross-validation. Strongest performance was observed with support vector regression, with 78% of predictions within twofold of the observed exposure changes. The results show that changes in drug exposure can be predicted with reasonable accuracy using regression-based machine-learning models trained on data available early in drug discovery. This has potential applications in enabling earlier drug-drug interaction risk assessment for new drug candidates.

Evaluation of US Food and Drug Administration Drug Label Recommendations for Coadministration of Antivirals and Acid-Reducing Agents.

Coadministration with acid-reducing agents (ARAs), including proton pump inhibitors (PPIs), histamine H2 -receptor antagonists (H2 blockers), and antacids has been demonstrated to reduce antiviral exposure and efficacy. Therefore, it is essential that US Food and Drug Administration (FDA) drug labels include recommendations to manage these drug-drug interactions (DDIs). This investigation analyzed information in FDA drug labels to manage DDIs between ARAs and antivirals approved from 1998 to 2019. To ascertain clinical adoption, we assessed whether FDA label recommendations were incorporated into current antiviral clinical practice guidelines. We identified 82 label recommendations for 43 antiviral approvals. Overall, 56.1% of recommendations were deemed clinically actionable, with the most common actionable management strategies being dose adjustment during coadministration (40.2%) and coadministration not recommended (9.8%). The sources informing DDI recommendations were clinical DDI studies (59.8%) and predictions of altered exposure (40.2%). Antivirals with low aqueous solubility were more likely to have label recommendations and were more commonly investigated using clinical DDI studies (P < 0.01). For recommendations informed by clinical DDI studies, changes in drug exposure were associated with actionable label recommendations (P < 0.01). The frequency of exposure changes in clinical DDI studies was similar across antiviral indications, but exposure changes were numerically higher for antacids (71.4%) relative to PPIs (42.9%) and H2 blockers (28.6%). Of DDI pairs identified within drug labels, 76.8% were included in guidelines, and recommended management strategies were concordant in 90.5% of cases. Our findings demonstrate that current regulatory oversight mostly (but not completely) results in actionable label recommendations to manage DDIs for high-risk antivirals.

Integrating real-world data to accelerate and guide drug development: A clinical pharmacology perspective.

Pharmaceutical products in the current accelerated drug development landscape can benefit from tools beyond data generated from randomized control trials. We have seen an abundance of real‐world data (RWD) and real‐world evidence, driven by the digitalization of healthcare systems and an increased awareness that has inspired a heightened interest in their potential use. Literature review suggest leveraging RWD as a promising tool to answer key questions in the areas of clinical pharmacology and translational science. RWD may increase our understanding regarding the impact of intrinsic (e.g., liver, renal impairment, or genetic polymorphisms) and extrinsic (e.g., food consumption or concomitant medications) factors on the clearance of administered drugs. Changes in clearance may lead to clinically relevant changes in drug exposure that may require clinical management strategies, such as change in dose or dosing regimen. RWD can be leveraged to potentially bridge the gaps among research, development, and clinical care. This paper highlights promising areas of how RWD have been used to complement clinical pharmacology throughout various phases of drug development; case examples will include dose/regimen extrapolation, dose adjustments for special populations (organ impairment, pediatrics, etc.), and pharmacokinetic/pharmacodynamic models to assess impact of prognostic factors on outcomes. In addition, this paper will also juxtapose limitations and promises of utilizing RWD to answer key scientific questions in drug development and articulate challenges posed by quality issues, data availability, and integration from various sources as well as the increased need for multidimensional‐omics data that can better guide the development of personalized and predictive medicine.

Harnessing Clinical Trial and Real-World Data Towards an Understanding of Sex Effects on Drug Pharmacokinetics, Pharmacodynamics and Efficacy.

Increasing clinical data on sex-related differences in drug efficacy and toxicity has highlighted the importance of understanding the impact of sex on drug pharmacokinetics and pharmacodynamics. Intrinsic differences between males and females, such as different CYP enzyme activity, drug transporter expression or levels of sex hormones can all contribute to different responses to medications. However, most studies do not include sex-specific investigations, leading to lack of sex-disaggregated pharmacokinetic and pharmacodynamic data. Based available literature, the potential influence of sex on exposure-response relationship has not been fully explored for many drugs used in clinical practice, though population-based pharmacokinetic/pharmacodynamic modelling is well-placed to explore this effect. The aim of this review is to highlight existing knowledge gaps regarding the effect of sex on clinical outcomes, thereby proposing future research direction for the drugs with significant sex differences. Based on evaluated drugs encompassing all therapeutic areas, 25 drugs demonstrated a clinically meaningful sex differences in drug exposure (characterised by ≥ 50% change in drug exposure) and this altered PK was correlated with differential response.

Bioequivalence assessment of high-capacity polymeric micelle nanoformulation of paclitaxel and Abraxane® in rodent and non-human primate models using a stable isotope tracer assay

The in vivo fate of nanoformulated drugs is governed by the physicochemical properties of the drug and the functionality of nanocarriers. Nanoformulations such as polymeric micelles, which physically encapsulate poorly soluble drugs, release their payload into the bloodstream during systemic circulation. This results in three distinct fractions of the drug-nanomedicine: encapsulated, protein-bound, and free drug. Having a thorough understanding of the pharmacokinetic (PK) profiles of each fraction is essential to elucidate mechanisms of nanomedicine-driven changes in drug exposure and PK/PD relationships pharmacodynamic activity. Here, we present a comprehensive preclinical assessment of the poly (2-oxazoline)-based polymeric micelle of paclitaxel (PTX) (POXOL hl-PM), including bioequivalence comparison to the clinically approved paclitaxel nanomedicine, Abraxane®. Physicochemical characterization and toxicity analysis of POXOL hl-PM was conducted using standardized protocols by the Nanotechnology Characterization Laboratory (NCL). The bioequivalence of POXOL hl-PM to Abraxane® was evaluated in rats and rhesus macaques using the NCL's established stable isotope tracer ultrafiltration assay (SITUA) to delineate the plasma PK of each PTX fraction. The SITUA study revealed that POXOL hl-PM and Abraxane® had comparable PK profiles not only for total PTX but also for the distinct drug fractions, suggesting bioequivalence in given animal models. The comprehensive preclinical evaluation of POXOL hl-PM in this study showcases a series of widely applicable standardized studies by NCL for assessing nanoformulations prior to clinical investigation.

Concomitant botanical medicine use among patients participating in commercial prostate cancer trials

ObjectivesPatients with cancer frequently use botanical medications. The concomitant use of such medications by patients on commercial trials has not been well-described, despite the importance of these trials for evaluating the safety and efficacy of new agents. We sought to describe the use of botanical medications taken by patients with prostate cancer enrolled on global commercial trials. DesignRetrospective study. SettingRegulatory repository of commercial clinical trial data. InterventionsAnti-cancer therapy. Main outcome measuresBotanical and medication use data were pooled across six international commercial randomized trials for metastatic prostate cancer with detailed information on medication and indications. Botanical products were considered to have potential for drug interaction if they led to a change in drug exposure in human trials. Potential for interaction was ascertained by PubMed review. Descriptive statistics were used for analysis. ResultsOf 7318 enrolled patients, 700 (10 %) reported botanical use at any time and 653 (9%) reported use of botanical products while on trial. Nearly half of botanical product types were not classified by plant (43 %). The highest proportion of botanical use was among patients in Asian countries (32 %), followed by patients in North America (13 %). Eighty-six different types of botanical products were used; of these, nineteen had a patient-reported anti-cancer indication. ConclusionsBotanical medicine use among patients with prostate cancer in commercial trials is moderate, although it varies by region. Practitioners should be aware of the use of botanical interventions in a clinical trial context.

Opportunities for using in silico-based extended dosing regimens for monoclonal antibody immune checkpoint inhibitors.

Therapeutic drug monitoring (TDM) involves frequent measurements of drug concentrations to ensure levels remain within a therapeutic window, and it is especially useful for drugs with narrow therapeutic indices or extensive interindividual pharmacokinetic variability. This technique has never been applied to immuno-oncology drugs, but, given recent examinations of clinical data (both exposure and response) on a number of these drugs, further investigations into TDM may be justified to reduce costs as well as potentially reducing the severity and/or duration of immune-related adverse events. Specifically, all but one of the approved PD-1 and PD-L1 inhibitors (pembrolizumab, nivolumab, cemiplimab-rwlc, atezolizumab, avelumab, durvalumab) have been shown to exhibit a plateaued exposure-response (E-R) curve at doses evaluated extensively to date, as well as time-dependent changes in drug exposure. Furthermore, responders have a greater decrease in drug clearance over time and would, therefore, have supratherapeutic serum concentrations. With frequent trough measurements, it is possible to use pharmacokinetic modelling and simulation to estimate drug clearance via Bayesian methods. Based on patient-specific estimates for clearance, optimal alternative dosing strategies can be simulated to lower drug and cost burden yet maintain therapeutic levels, especially as the clearance of the drug decreases over time. This review will comprehensively discuss each of the FDA approved PD-1, PD-L1/2 and CTLA-4 inhibitors regarding their indications and current recommended dosing, with evidence supporting the investigation of these types of TDM strategies.

Physiologically Based Pharmacokinetic Modelling to Identify Pharmacokinetic Parameters Driving Drug Exposure Changes in the Elderly.

Medication use is highly prevalent with advanced age, but clinical studies are rarely conducted in the elderly, leading to limited knowledge regarding age-related pharmacokinetic changes. The objective of this study was to investigate which pharmacokinetic parameters determine drug exposure changes in the elderly by conducting virtual clinical trials for ten drugs (midazolam, metoprolol, lisinopril, amlodipine, rivaroxaban, repaglinide, atorvastatin, rosuvastatin, clarithromycin and rifampicin) using our physiologically based pharmacokinetic (PBPK) framework. PBPK models for all ten drugs were developed in young adults (20-50years) following the best practice approach, before predicting pharmacokinetics in the elderly (≥ 65years) without any modification of drug parameters. A descriptive relationship between age and each investigated pharmacokinetic parameter (peak concentration [Cmax], time to Cmax [tmax], area under the curve [AUC], clearance, volume of distribution, elimination-half-life) was derived using the final PBPK models, and verified with independent clinically observed data from 52 drugs. The age-related changes in drug exposure were successfully simulated for all ten drugs. Pharmacokinetic parameters were predicted within 1.25-fold (70%), 1.5-fold (86%) and 2-fold (100%) of clinical data. AUC increased progressively by 0.9% per year throughout adulthood from the age of 20years, which was explained by decreased clearance, while Cmax, tmax and volume of distribution were not affected by human aging. Additional clinical data of 52 drugs were contained within the estimated variability of the established age-dependent correlations for each pharmacokinetic parameter. The progressive decrease in hepatic and renal blood flow, as well as glomerular filtration, rate led to a reduced clearance driving exposure changes in the healthy elderly, independent of the drug.

PDG16 PREDICTING THE COST-EFFECTIVENESS OF GENERIC VERSUS BRAND DABIGATRAN USING PHARMACOMETRIC ESTIMATES AMONG PATIENTS WITH ATRIAL FIBRILLATION

Challenges for generic products to achieve bioequivalence may occur when medications have complex pharmaceutical properties or narrow therapeutic windows. While generic substitution usually results in cost savings, the overall medical expenses and cost-effectiveness of replacing brand medications can be difficult to predict. This study aimed to estimate the “pre-launch” cost-effectiveness of generic dabigatran versus the reference brand in atrial fibrillation (AF) patients utilizing estimated effectiveness and safety parameters for bioequivalence thresholds. A Markov micro-simulation (12 states) was adapted to predict the lifetime costs and quality-adjusted survival (QALYs) of three versions of dabigatran, the brand product and two hypothetical generic versions assuming extremes of bioavailability at 80% and 125% of systemic exposure, according to the U.S. private payer’s perspective. The hypothetical cohort consisted of 10,000 AF patients with age, CHADS2-VASc and HAS-BLED scores similar to a commercially insured AF patient cohort (Truven Health MarketScan®). Transition probabilities, estimated from Truven Health MarketScan® and literature were dependent upon patient characteristics. Differences in stroke and bleeding rates between generic and brand were obtained from pharmacokinetic-pharmacodynamic (PK/PD) models of Cmax and area under the curve changes in drug exposure. Costs and utilities were from the literature. The lower-bound (80% systemic exposure) generic dabigatran was dominant, with the highest quality-adjusted life years (7.31 QALYs) and lowest cost ($49,368.27) among the three drugs. Comparison between higher-bound generic (125% systemic exposure) and brand dabigatran showed that the brand was more cost-effective with an incremental cost-effectiveness ratio of $25,193/QALY. The simulation showed that among the three drugs, the lower-bound generic version is the most cost-effective option. However, the higher-bound generic version is not cost-effective when compared with brand dabigatran. While both the PK/PD and cost-effectiveness models are projections of future generic products, these models can be informative for regulatory bodies and in the clinical setting to determine appropriate therapy.

A novel role for E2F3b in regulating cocaine action in the prefrontal cortex.

Drug abuse is a multifaceted disorder that involves maladaptive decision making. Long-lasting changes in the addicted brain are mediated by a complex circuit of brain reward regions. The prefrontal cortex (PFC) is one region in which chronic drug exposure changes expression and function of upstream transcriptional regulators to alter drug responses and aspects of the addicted phenotype. We reported recently that the transcription factor E2F3a is a critical mediator of cocaine responses in the nucleus accumbens. E2F3a is one of two splice variants of the E2f3 gene; the other is E2F3b. Another recent study predicted E2F3 as an upstream regulator of the transcriptional response to cocaine self-administration (SA)in PFC. Based on previous findings that E2F3a and E2F3b have divergent regulatory roles, we set out to study the putative transcriptional role of these transcripts in PFC in the context of repeated I.P. cocaine exposure. We implemented viral-mediated isoform-specific gene manipulation, RNA-sequencing, advanced bioinformatics analyses, and animal behavior to determine how E2F3a and E2F3b contribute to persistent cocaine-induced transcriptional changes in PFC. We show that E2F3b, but not E2F3a, in PFC is critical for cocaine locomotor and place preference behaviors. Interestingly, RNA-seq of PFC following E2f3b overexpression or I.P. cocaine exposure showed very different effects on expression levels of differentially expressed genes. However, we found that E2F3b drives a similar transcriptomic pattern to that of cocaine SA with overlapping upstream regulators and downstream pathways predicted. These findings reveal a novel transcriptional mechanism in PFC that controls behavioral and molecular responses to cocaine.

Are Clinical Therapeutic Protein-Drug Interaction Studies Needed When Disease Modification Is Driving the Potential Interaction?

Dear Editor, We read with great interest the article by Coutant and Hall1 recently published in The Journal of Clinical Pharmacology regarding disease-drug interactions in inflammatory states. This article provides a comprehensive review of the impact of inflammatory diseases on drug clearance via cytokine-cytochrome P450 (CYP)-mediated disease-drug interactions, with a conclusion that in most cases inflammatory disease–drug interactions are not expected to be of clinical concern. The information in this article raises some important questions regarding therapeutic protein–drug interaction (TP-DI) assessments for therapeutic proteins being developed for the treatment of inflammatory diseases. This topic is of particular interest given that the updated 2017 Food and Drug Administration guidance2 does not include discussion on therapeutic proteins and that the Food and Drug Administration is currently seeking public comments on the optimal regulatory framework in which to assess TP-DIs.3 We would like to point out several key items that should be taken into consideration when evaluating the TP-DI potential for therapies targeting proinflammatory cytokines. First, though comparisons of cytokine levels between patients and healthy subjects may be useful for understanding extreme differences in drug clearance between inflammatory disease and healthy status,1 such comparisons may overestimate the magnitude of difference in drug exposure caused by TP-DIs mediated through disease/cytokine modulation by therapeutic proteins. In fact, most of the disease-mediated TP-DIs due to normalization/suppression of proinflammatory cytokines have shown at most only mild to moderate effects on drug exposures and have rarely led to a dose adjustment for the victim drug (AbbVie data on file for risankizumab and for other related TP-DIs4-8). Second, dedicated TP-DI studies are typically conducted in patients, without the full course of the proposed therapeutic protein treatment for therapeutic benefit and disease modification. Such studies put patients at risk for developing antidrug antibodies, which could negatively affect future treatment. Therefore, the scientific benefit gained from conducting TP-DI studies does not necessarily outweigh the risks. Third, drug interactions mediated through disease/cytokine modification raise different considerations than other types of drug interactions. As pointed out by Coutant and Hall,1 the net effect of treatment in this case is reduced cytokine levels, leading to partial restoration of CYP enzyme and drug transporter activities, potentially resulting in mild reduction of victim drug exposures. As such, reduced efficacy, rather than toxicity, is the likely outcome. There are only a few sensitive CYP substrates with a narrow therapeutic index for which a small change in exposure would fall outside the desired therapeutic range, and these should be closely monitored anyway. Fourth, similar to therapeutic proteins, small-molecule drugs have the same potential to cause drug interactions if they reduce cytokine levels via disease modification, yet such assessments for small-molecule drugs are neither performed nor required by regulatory guidelines, indicating the lack of necessity or clinical relevance of such evaluations. The ultimate goal of drug interaction studies is to determine the magnitude of change in drug exposure that can be expected clinically so that labeling can be put into place. However, based on the points discussed above, when changes in drug exposure are driven by disease modification, conventional drug interaction studies are often not clinically informative. It is our opinion that dedicated TP-DI studies are generally not needed, especially for the following 2 scenarios: (1) For new therapeutic proteins that target pathways previously studied for other therapeutic proteins, TP-DI studies are not needed if the previously studied therapeutic protein has demonstrated little or no TP-DI potential; and (2) if a therapeutic protein is being developed for multiple indications and the therapeutic protein has been shown to have little or no TP-DI potential in the disease with the most severe inflammatory burden, TP-DI studies are not needed in any new indications that have a less severe inflammatory burden. For a novel target pathway, data on cytokines that have known effects on CYPs or transporters can be collected before and after therapeutic protein treatment in phase 2/3 studies to guide the TP-DI risk assessment, and, if needed, a TP-DI substudy can be incorporated into the phase 2/3 studies to evaluate the impact of the therapeutic protein on narrow therapeutic index drugs. We believe that continuing to default to dedicated TP-DI studies for therapeutic proteins targeting inflammatory diseases is not supported by available scientific evidence, and drug development standards need to be revised accordingly. The authors are employees and shareholders of AbbVie.

Analysis of Clinical Drug-Drug Interaction Data To Predict Magnitudes of Uncharacterized Interactions between Antiretroviral Drugs and Comedications.

ABSTRACTDespite their high potential for drug-drug interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also being ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required. We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes, such as CYP3A, and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers, and inhibitors of CYP3A to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs. The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n = 68) between ARVs and comedications were successfully quantified, meaning 53%, 85%, and 98% of the predictions were within 1.25-fold (0.80 to 1.25), 1.5-fold (0.66 to 1.48), and 2-fold (0.66 to 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or, conversely, minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs. The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs, being particularly relevant in an HIV setting, given the treatment's complexity, high DDI risk, and limited guidance on the management of DDIs.

Oral drug absorption in pediatrics: the intestinal wall, its developmental changes and current tools for predictions.

The dissolution, intestinal absorption and presystemic metabolism of a drug depend on its physicochemical characteristics but also on numerous physiological (e.g. gastrointestinal pH, volume, transit time, morphology) and biochemical factors (e.g. luminal enzymes and flora, intestinal wall enzymes and transporters). Over the past decade, evidence has accumulated indicating that these factors may differ in children and adults resulting in age‐related changes in drug exposure and drug response. Thus, drug dosage may require adjustment for the pediatric population to ensure the desired therapeutic outcome and to avoid side‐effects. Although tremendous progress has been made in understanding the effects of age on intestinal physiology and function, significant knowledge gaps remain. Studying and predicting pharmacokinetics in pediatric patients remains challenging due to ethical concerns associated with clinical trials in this vulnerable population, and because of the paucity of predictive in vitro and in vivo animal assays. This review details the current knowledge related to developmental changes determining intestinal drug absorption and pre‐systemic metabolism. Supporting experimental approaches as well as physiologically based pharmacokinetic modeling are also discussed together with their limitations and challenges. © 2016 UCB Biopharma sprl. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd.

Narcotics and Sedative Use in Preterm Neonates

To evaluate patterns of narcotic and sedative use in neonatal intensive care units (NICUs) across Canada using data collected by the Canadian Neonatal Network. We conducted a retrospective observational cohort study of preterm neonates at <33 weeks' gestation and admitted to a participating Canadian Neonatal Network NICU. The proportion of all neonates who received sedative(s), narcotic(s), or either sedative(s), narcotic(s), or both during their NICU stay was calculated for each year. Because opioids are used for premedication before intubation, only continuous infusions of a narcotic drug were included. Variation in narcotics and sedative usage between sites in 2014 was determined using logistic regression analysis, with adjustment for gestational age, surgery, and mechanical ventilation. Of 20 744 neonates, 29% of neonates received a narcotic, a sedative, or both; 23% received a narcotic and 17% a sedative. Although no clinically significant changes in drug exposure were documented during the 5-year period, there were statistically significant differences in narcotic and sedative use between sites, ranging from 3% to 41% for narcotic and 2% to 48% for sedative use (aORs 0.2-5.7 and 0.1-15, respectively, P < .05). Exposure to narcotic or sedative agents is highly variable in preterm neonates across Canada despite concerns of adverse outcomes associated with these drugs. The tremendous variation in practice suggests that further research on their current usage, as well as identifying optimal practice procedures is warranted.