B‐cell maturation antigen (BCMA)‐targeting immunotherapies (e.g., chimeric antigen receptor T cells (CAR‐T) and bispecific antibodies (BsAbs)) have achieved remarkable clinical responses in patients with relapsed and/or refractory multiple myeloma (RRMM). Their use is accompanied by exaggerated immune responses related to T‐cell activation and cytokine elevations leading to cytokine release syndrome (CRS) in some patients, which can be potentially life‐threatening. However, systematic evaluation of the risk of CRS with BCMA‐targeting BsAb and CAR‐T therapies, and comparisons across different routes of BsAb administration (intravenous (i.v.) vs. subcutaneous (s.c.)) have not previously been conducted. This study utilized a meta‐analysis approach to compare the CRS profile in BCMA‐targeting CAR‐T vs. BsAb immunotherapies administered either i.v. or s.c. in patients with RRMM. A total of 36 studies including 1,560 patients with RRMM treated with BCMA‐targeting CAR‐T and BsAb therapies were included in the analysis. The current analysis suggests that compared with BsAbs, CAR‐T therapies were associated with higher CRS incidences (88% vs. 59%), higher rates of grade ≥ 3 CRS (7% vs. 2%), longer CRS duration (5 vs. 2 days), and more prevalent tocilizumab use (44% vs. 25%). The proportion of CRS grade ≥ 3 may also be lower (0% vs. 4%) for BsAb therapies administered via the s.c. (3 studies, n = 311) vs. i.v. (5 studies, n = 338) route. This meta‐analysis suggests that different types of BCMA‐targeting immunotherapies and administration routes could result in a range of CRS incidence and severity that should be considered while evaluating the benefit–risk profiles of these therapies.

Poor and variable oral bioavailability of furosemide (FUR) presents critical challenges in pharmacotherapy. We investigated the interplay of breast cancer resistance protein (Bcrp)-mediated transport, sex, and fed state on FUR pharmacokinetics (PK) in rats. A crossover PK study of FUR (5 mg/kg, oral) was performed in Sprague-Dawley rats (3 males and 3 females), alone or with a Bcrp inhibitor, novobiocin (NOV) (20 mg/kg, oral), in both fed and fasted states. Co-administration of NOV significantly increased FUR extent (AUC) and rate (Cmax) of exposure by more than two-fold, which indicates efficient Bcrp inhibition in the intestine. The female rats showed two-fold higher AUC and Cmax, and two-fold lower renal clearance of FUR compared to the male rats. The latter was correlated with higher renal abundance of Bcrp and organic anion transporters (Oats) in the male rats compared to age-matched female rats. These findings suggest that the PK of Bcrp and/or Oat substrates could be sex-dependent in rats. Moreover, allometric scaling of rat PK and toxicological data of Bcrp substrates should consider species and sex differences in Bcrp and Oat abundance in the kidney. Considering that Bcrp is abundant in the intestine of rats and humans, a prospective clinical study is warranted to evaluate the effect of Bcrp inhibition on FUR PK. The potential confounding effect of the Bcrp transporter should be considered when FUR is used as a clinical probe of renal organic anion transporter-mediated drug–drug interactions. Unlike human data, no food-effect was observed on FUR PK in rats.

The effect of food on oral drug absorption is determined by the complex interplay among gut physiological factors and drug properties. The currently used dissolution testing and classification systems (biopharmaceutics classification system, BCS or biopharmaceutics drug disposition classification system, BDDCS) do not account for dynamic changes in gastrointestinal physiology caused by food intake. This study aimed to identify key drug properties that influence food effect (FE) using supervised machine learning approaches. The analysis showed that drugs with high logP, dose number, and extraction ratio have a higher probability of positive FE, while drugs with low permeability and high efflux saturation index have a greater likelihood of negative FE. Weakly acidic drugs also showed a greater probability of positive FE, particularly at pKa >4.3. The importance of drug properties in predicting FE was ranked as logP, dose number, extraction ratio, pKa, and permeability. The accuracy of FE prediction using the models was compared with BCS and extended clearance classification system (ECCS). Overall, the likelihood or magnitude of FE depends on physiological changes to food intake such as altered bile acid secretion rate, intestinal metabolism, transport kinetics, and gastric emptying time, which should be considered along with drug properties (e.g., solubility, logP, and ionization) in predicting FE of orally administered drugs.

Rats are extensively used as a preclinical model for assessing drug pharmacokinetics (PK) and tissue distribution; however, successful translation of the rat data requires information on the differences in drug metabolism and transport mechanisms between rats and humans. To partly fill this knowledge gap, we quantified clinically relevant drug-metabolizing enzymes and transporters (DMETs) in the liver and different intestinal segments of Sprague-Dawley rats. The levels of DMET proteins in rats were quantified using the global proteomics-based total protein approach (TPA) and targeted proteomics. The abundance of the major DMET proteins was largely comparable using quantitative global and targeted proteomics. However, global proteomics-based TPA was able to detect and quantify a comprehensive list of 66 DMET proteins in the liver and 37 DMET proteins in the intestinal segments of SD rats without the need for peptide standards. Cytochrome P450 (Cyp) and UDP-glycosyltransferase (Ugt) enzymes were mainly detected in the liver with the abundance ranging from 8 to 6502 and 74 to 2558 pmol/g tissue. P-gp abundance was higher in the intestine (124.1 pmol/g) as compared to that in the liver (26.6 pmol/g) using the targeted analysis. Breast cancer resistance protein (Bcrp) was most abundant in the intestinal segments, whereas organic anion transporting polypeptides (Oatp) 1a1, 1a4, 1b2, and 2a1 and multidrug resistance proteins (Mrp) 2 and 6 were predominantly detected in the liver. To demonstrate the utility of these data, we modeled digoxin PK by integrating protein abundance of P-gp and Cyp3a2 into a physiologically based PK (PBPK) model constructed using PK-Sim software. The model was able to reliably predict the systemic as well as tissue concentrations of digoxin in rats. These findings suggest that proteomics-informed PBPK models in preclinical species can allow mechanistic PK predictions in animal models including tissue drug concentrations.

Introduction: B-cell maturation antigen (BCMA)-targeting immunotherapies (eg, chimeric antigen receptor T cells (CAR-T) and bispecific antibodies (BsAb)) have achieved remarkable clinical responses in patients (pts) with relapsed and/or refractory multiple myeloma (RRMM). Their use is accompanied by exaggerated immune responses related to T cell activation and cytokine elevations leading to cytokine release syndrome (CRS), which could be life-threatening if presenting in high grades. This study utilized a meta-analysis approach to compare CRS profile in BCMA-targeting CAR-Ts vs. BsAbs in pts with RRMM. Methods: CRS profiles for BCMA-targeting CAR-T and BsAb therapies were compiled from publicly available data in published literature up to July 2022. The meta-analysis included 28 CAR-Ts, including 2 approved therapies (idecabtagene vicleucel and ciltacabtagene autoleucel) and 8 investigational BsAb. The weighted proportion of all Grades, Grade 1-2, and Grade ≥3 CRS was estimated using fixed- and random-effects meta-analytic models. Between-study heterogeneity was evaluated using the heterogeneity index (I2) and P-value. Subgroup analyses were performed to assess the differences in CRS incidence and severity across CAR-T and BsAb and routes of BsAb administration (IV vs. SC). Dosing, priming/premedication (premeds) regimens, time to onset, duration of CRS, and CRS management were compared. Results: A total of 53 studies including 2092 RRMM pts treated with BCMA-targeting CAR-T and BsAb therapies were included in the analysis. The meta-analysis showed a significantly higher weighted proportion of CRS with CAR-T therapies vs. BsAb (all Grades 87% (95% CI: 80-93) vs. 67% (95% CI: 58-75), I2= 87%, P-value <0.01; and Grade ≥3 6% (95% CI: 3-9) vs. 0.2% (95% CI: 0.0-1.7), I2= 65%; P-value <0.01 (Figure 1). Average time to CRS onset was delayed (5 days vs. 1 day) and the median duration of CRS was longer (5 days vs. 2 days) after CAR-T compared to BsAb. Use of premeds (eg, corticosteroids, antihistamines, and antipyretics) was reported for 5 BsAb agents, while premeds were not used to reduce CRS severity with CAR-Ts. Tocilizumab and corticosteroids were used to manage CRS in 50% (325/649 pts; 12 studies) and 17% (87/525 pts; 9 studies) of patients, respectively, treated with CAR-T therapies, as compared to 31% (212/677 pts; 13 studies) and 16% (102/625 pts; 9 studies) of patients, respectively, treated with BsAb agents. Two BsAbs evaluated both IV and SC routes (Table 1). A higher incidence of CRS Grade 1-2 was found following SC compared to IV route of BsAb administration: 73% (95% CI: 62-83) vs. 58% (95% CI: 48-68) (I2=73%; P-value= 0.05), respectively. However, higher doses were evaluated with SC compared to IV routes. A higher incidence of CRS Grade ≥3 was observed with BsAb administered IV vs. SC: 0.9% (95% CI: 0.0-4%) vs. 0.0% (95% CI: 0.0-0.3%), respectively (I2= 49%; P-value = 0.05). CRS occurrence by dose number administered was reported for 3 BsAbs. Except for teclistamab, which reported CRS events in 4.8%, 2.4%, 1.2% and 3.6% of pts following the 4th, 5th, 6th, and later doses, respectively, the other 2 BsAbs (CC- 93269 and elranatamab) reported CRS episodes mostly following either the first or second dose, and rarely with the third or later doses. Conclusions: The current analysis showed BCMA CAR-T therapies to be associated with higher CRS incidence, higher rates of Grade ≥3 CRS, longer CRS duration, and more prevalent tocilizumab and steroid use for CRS management compared to BsAb, possibly due to a) higher cytokine production following CAR-T therapies and/or b) use of premedication and priming regimens with BsAb which may improve the CRS profile (Table 1). Despite evaluation of a higher dose range, the proportion of CRS Grades ≥3 was higher with BsAb dosed IV vs. SC suggesting that the SC route of administration may allow exploration of higher doses while reducing the incidence of high-grade CRS. This meta-analysis showed that different types of BCMA-targeting immunotherapies and administration routes could contribute to different CRS profiles. The analysis is limited to published literature, and biases due to missing data or different reporting approaches cannot be excluded (eg, several BsAbs reported CRS rates pooled across doses). The analysis will be updated upon availability of full publications with more granular data. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Dimethandrolone (DMA), an active metabolite of dimethandrolone undecanoate (DMAU) is a novel derivative of 19-nortestosterone that is undergoing clinical investigation as an experimental male hormonal contraceptive.1 However, poor and variable oral bioavailability of DMAU including dramatic food-effect2 may decrease its potential use by oral route. Moreover, DMA to DMAU serum concentration ratios revealed that food significantly alters the first-pass metabolism of DMAU or DMA (Fig. 1A). Thus, the objective of this study was to investigate intestinal and hepatic metabolism of the active metabolite, DMA, to understand its poor bioavailability. We first detected and elucidated the structures of DMA metabolites formed in human hepatocyte incubations and in serum samples after oral administration in men using nano-liquid chromatography high-resolution mass spectrometry. The accurate mass and the mass fragmentation data confirmed DMA (m/z 303.2318) analogues of androstenedione (m/z 301.2162) and glucuronide (m/z 479.2639) in the human hepatocyte incubation, however, only DMA-glucuronide was detected in the serum samples following an oral dose of 400 mg DMAU. DMA-glucuronide serum concentration was >100-fold higher than DMA levels, revealing glucuronidation as the major elimination mechanism for DMA. The targeted metabolomics of DMA revealed that unlike testosterone, DMA is not metabolized by other androgen metabolizing enzymes such as aromatase and 5-α- and 5-β- reductases, likely due to the absence of the C19-methyl group.3,4 Next, to identify UGT isoforms involved in DMA metabolism, thirteen clinically-relevant UGT enzymes (UGT1A1, 1A3, 1A4, 1A6, 1A7, 1A8, 1A9, 1A10, 2B4, 2B7, 2B10, 2B15, and 2B17) were screened for their capacity to metabolize DMA. Only UGT2B17 and UGT1A9 were able to metabolize DMA to DMA-glucuronide. Finally, since UGT2B17 is a highly variable enzyme (>3000-fold variability) with highly prevalent gene deletion, an enzyme kinetic experiment was performed in human intestinal microsomes (HIM) samples with known UGT2B17 expression. The kinetics data revealed >200-fold higher maximum reaction velocity (Vmax) in the high UGT2B17-expressing HIM samples as compared to the null expressors (Fig. 1B). Proteomics-informed modeling estimated the intestinal fractional contributions (fm) of UGT2B17 in DMA glucuronidation to be 0.996, 0.994, and 0.0 in the high, average, and null expressors of UGT2B17, respectively. These data suggest that genetic variations in UGT2B17 and its high-intestinal abundance are the potential reasons for the variable first-pass metabolism and oral pharmacokinetics of DMA, whereas UGT1A9, which is expressed in the liver and kidney, likely decreases fm, UGT2B17 in DMA metabolism after intramuscular dosing. Refs. 1. Attardi B J (2010). 2. Ayoub R (2017). 3. Attardi B J (2008). 4. Han Y (2021).

Dimethandrolone undecanoate (DMAU), an oral investigational male hormonal contraceptive, is a prodrug that is rapidly converted to its active metabolite, dimethandrolone (DMA). Poor and variable oral bioavailability of DMA after DMAU dosing is a critical challenge to develop it as an oral drug. The objective of our study was to elucidate the mechanisms of variable pharmacokinetics of DMA. We first identified DMA metabolites formed in vitro and in vivo in human hepatocyte incubation and serum samples following oral DMAU administration in men, respectively. The metabolite identification study revealed two metabolites, DMA-glucuronide (DMA-G; major) and the androstenedione analog of DMA (minor), in the hepatocyte incubations. After oral DMAU administration, only DMA-G was detected in serum, which was >100-fold compared with DMA levels, supporting glucuronidation as the major elimination mechanism for DMA. Next, 13 clinically relevant UDP-glucuronosyltransferase (UGT) enzymes were tested for their involvement in DMA-G formation, which revealed a major role of UDP-glucuronosyltransferase 2B17 (UGT2B17) isoform with a smaller contribution of UGT1A9 in DMA-G formation. These data were confirmed by dramatically higher DMA glucuronidation rates (>200- and sevenfold) in the high versus the null UGT2B17-expressing human intestinal and liver microsomes, respectively. Since human UGT2B17 is a highly variable enzyme with a 20%-80% gene deletion frequency, the in vitro data suggest a major role of UGT2B17 polymorphism on the first-pass metabolism of DMA. Further, considering DMA is a selective and sensitive UGT2B17 substrate, it could be used as a clinical probe of UGT2B17 activity. SIGNIFICANCE STATEMENT: Dimethandrolone (DMA) is an active metabolite of dimethandrolone undecanoate (DMAU), an investigational male hormonal contraceptive. Previous studies have indicated poor and inconsistent bioavailability of DMAU following oral administration. This study found that UDP-glucuronosyltransferase 2B17-mediated high intestinal first-pass metabolism is the key mechanism of variable DMA bioavailability.

A reliable translation of in vitro and preclinical data on drug absorption, distribution, metabolism, and excretion (ADME) to humans is important for safe and effective drug development. Precision medicine that is expected to provide the right clinical dose for the right patient at the right time requires a comprehensive understanding of population factors affecting drug disposition and response. Characterization of drug-metabolizing enzymes and transporters for the protein abundance and their interindividual as well as differential tissue and cross-species variabilities is important for translational ADME and precision medicine. This review first provides a brief overview of quantitative proteomics principles including liquid chromatography-tandem mass spectrometry tools, data acquisition approaches, proteomics sample preparation techniques, and quality controls for ensuring rigor and reproducibility in protein quantification data. Then, potential applications of quantitative proteomics in the translation of in vitro and preclinical data as well as prediction of interindividual variability are discussed in detail with tabulated examples. The applications of quantitative proteomics data in physiologically based pharmacokinetic modeling for ADME prediction are discussed with representative case examples. Finally, various considerations for reliable quantitative proteomics analysis for translational ADME and precision medicine and the future directions are discussed. SIGNIFICANCE STATEMENT: Quantitative proteomics analysis of drug-metabolizing enzymes and transporters in humans and preclinical species provides key physiological information that assists in the translation of in vitro and preclinical data to humans. This review provides the principles and applications of quantitative proteomics in characterizing in vitro, ex vivo, and preclinical models for translational research and interindividual variability prediction. Integration of these data into physiologically based pharmacokinetic modeling is proving to be critical for safe, effective, timely, and cost-effective drug development.

Two patients presented with hard black eschars covering granulating ulcers. The ulcers healed within 6 weeks to leave small scars. The diagnosis of cowpox was confirmed by serology in both cases and in addition by polymerase chain reaction in the first. The source of infection was likely to be a rodent in the first case whilst the second was more typical in being transmitted by a cat.

This commentary presents contributions and accomplishments of Professor Saranjit Singh, National Institute of Pharmaceutical Education and Research (NIPER), SAS Nagar, India, to pharmaceutical research and education. Prof. Singh completed his successful tenure in October 2021. Over his 40+ years of illustrious academic career, he trained 147 Masters and 15 PhD students in the fields of drug stability testing, degradation chemistry, impurity and metabolite characterization, and advanced analytical technologies. He has published ∼250 research articles, reviews, editorials, patent, book, and book chapters, and received numerous awards, including the Professor M.L. Khorana Memorial Lecture Award from the Indian Pharmaceutical Association (IPA) and the Outstanding Analyst and Eminent Analyst awards from the Indian Drug Manufacturers' Association (IDMA). This commentary highlights Prof. Singh's inspiring personal and renowned professional journey, including early life, education, career, accomplishments, as well as his services to academia, industry, and regulatory. By sharing the contributions and accomplishments of Prof. Singh, we strongly believe that his story will inspire the next generation of scientists to continue his legacy to advance the field.

The oral route of drug administration is the most convenient method of drug delivery, but it is associated with variable bioavailability. Food is one of the major factors that affect oral drug absorption by influencing drug properties (e.g., solubility and dissolution rate) and physiological factors (e.g., metabolism and transport across the gastrointestinal tract). The aim of this work was to investigate the effect of food on the high-affinity intestinal efflux transporter substrate drugs. We hypothesized that transport efficiency is higher in the fed state as compared to the fasted state because of the lower intestinal lumen drug concentration due to prolonged gastric emptying time. A systematic analysis of reported clinical food-effect (FE) studies on 311 drugs was performed and the association of the efflux transport efficiency was investigated on the FE magnitude, i.e., changes in maximal plasma concentration and area under the plasma concentration–time profile curve for both solubility and permeability-limited drugs. In total, 124 and 88 drugs showed positive and negative FE, respectively, whereas 99 showed no FE. As expected, the solubility-limited drugs showed positive FE, but interestingly, drugs with a high potential for efflux transport, were associated with negative FE. Moreover, a high-fat diet was associated with a higher magnitude of negative FE for high-affinity efflux transporter substrates as compared to a low-fat diet. To account for changes in drug absorption after food intake, the prolonged gastric emptying time should be considered in the physiologically based pharmacokinetic (PBPK) modeling of orally absorbed efflux transporter substrate drugs.

In vitro dissolution testing to estimate the likelihood of food‐effect for the solubility‐limited drugs is recommended by the regulatory agencies; however, the effect of changes in physiology such as delayed gastric emptying after food intake cannot be simulated in a dissolution apparatus. To address this, we performed metanalysis of food‐effect studies and investigated the effect of food on drug bioavailability due to the changes in physiology and drug properties in fed‐state. Our specific aims were to: i) identify the determinants of food‐effect on drug absorption, ii) determine the magnitude by which fed‐state associated physiological changes affect the absorption of efflux transporter substrates, and iii) measure the effect of high‐fat versus low‐fat diets on the magnitude of food‐effect for the efflux transporter substrates. A systematic meta‐analysis of clinical food‐effect studies on 311 drugs was performed to evaluate the association of food‐effect magnitude with drug‐ and physiology‐specific properties (i.e., change in maximal plasma concentration, Cmax, and area under the plasma concentration‐time profile curve, AUC). Of the studied drugs, 124 and 88 drugs showed positive and negative food‐effects, respectively, whereas 99 showed no food‐effect. Five deterministic properties that influence food‐effect were identified, i.e., logP, log dose number, metabolic extraction ratio (EH), and saturation indices of intestinal apical uptake and efflux transporters (Equation 1). In particular, a majority of the drugs with efficient efflux transport (saturation index ≥2) showed negative food‐effect. Since these data cannot be explained by dissolution testing, the negative food‐effect for the efflux transporter substrates is likely a result of prolonged gastric emptying time in fed‐state that leads to increased efflux transport efficiency (luminal drug concentration < Michaelis‐Menten constant, Km) (Figure 1A). For example, drugs with saturation index ≥2, such as didanosine, voriconazole, erythromycin, omadacycline, and asciminib, showed 50‐67% decrease in Cmax and AUC. Moreover, prolonged gastric emptying time with high‐fat diet is associated with a greater negative food‐effect as compared to low‐fat diet conditions in these transporter substrates. For example, omadacycline, eltrombopag, indinavir, and asciminib showed significantly decreased Cmax (P <0.01) and AUC (P <0.001) with high‐fat diet (Figure 1B). The present study is the first to confirm the impact of fed‐state associated prolonged gastric emptying time on the saturable kinetics of efflux transporter substrates. Preclinical validation of the postulated mechanism is ongoing in our laboratory. The understanding and integration of changes in physiology and drug properties will allow prospective prediction of food‐effect on oral drug absorption.

Food can influence drug bioavailability by altering gut physiology and microenvironment. Conventional approaches such as dissolution testing and biopharmaceutics classification system (BCS) often fail to predict food effect on drug bioavailability. To address this, we proposed a mechanistic BCS (mBCS) (Figure 1) based on analysing literature data. The mBCS first classifies drugs based on if their absorption is solubility‐limited (Class I) or permeability‐limited (Class II), which are sub‐grouped based on if the food effect is predictable (IA and IIA) or not predictable (IB, IC, IIB and IIC) using the dissolution testing (Figure 1A). Finally, mBCS allows identifying the mechanisms that lead to unpredictable food‐effect on drug bioavailability. Particularly, out of 288 drugs where the food‐effect data were reported, 105 drugs exhibited negative food effect. Further evaluation suggested that the primary reason for the unpredictable negative food effect is the interplay of intestinal efflux transport and delayed gastric emptying (Figure 1B). For example, amongst all drugs that showed negative food effect, majority (58%) were substrates of GI efflux transporters (e.g., maraviroc, afatinib, furosemide, ixazomib, asciminib, and omadacycline). We thus hypothesized that since gastric emptying time in fed state is higher (>2 h) as compared to the fasting state (15 min), drug co‐administration with food results in decreased concentration gradient across intestinal wall (lumen to blood), which eventually causes desaturation of GI efflux transporters. To test this hypothesis, we developed an mBCS‐guided physiologically based pharmacokinetic (PBPK) model in MATLAB (MathWorks, Natick, MA) by incorporating the regional protein abundance and saturation kinetics (Km and Vmax) of selected drugs (maraviroc, afatinib, and furosemide). The results from maraviroc PBPK modeling are presented below. The model was successful in predicting non‐linear PK within 2‐fold at varied dose ranges (e.g., 0.5, 0.61, 0.88, 1.17, 1.07 and 1.02 folds for 30, 100, 300, 600, 900 and 1200 mg, respectively for maraviroc) in the fasted state. The model successfully captured ~37% reduction in AUC in fed state at lower clinical doses of 100 and 300 mg of maraviroc. Similarly, consistent with the observed clinical data 1, the predicted magnitude of negative food effect was significantly lower (~22 and 18% reduction in AUC) at higher doses of 900 and 1200 mg, respectively. Parameter sensitivity analysis showed that gastric emptying time and kinetic parameters of P‐gp are important mechanisms that regulate non‐linear PK and food effect for maraviroc. Therefore, the proposed mBCS‐guided PBPK modeling approach can be used for prospective prediction of negative food effect, which can also be used to justify clinical trial waiver for the food effect studies.Support or Funding InformationDepartment of Pharmaceutical Sciences, Washington State University, SpokaneMechanistic biopharmaceutics classification system (mBCS) framework (A) and proposed mechanism of interplay between GI efflux and gastric emptying in fasting and fed states for mBCS class‐IIC drugs (B).Figure 1

Introduction: Pediatric patients, especially neonates and infants, are more susceptible to adverse drug events as compared to adults. In particular, immature small molecule drug metabolism and excretion can result in higher incidences of pediatric toxicity than adults if the pediatric dose is not adjusted. Area covered: We reviewed the top 29 small molecule drugs prescribed in neonatal and pediatric intensive care units and compiled the mechanisms of their metabolism and excretion. The ontogeny of Phase I and II drug metabolizing enzymes and transporters (DMETs), particularly relevant to these drugs, are summarized. The potential effects of DMET ontogeny on the metabolism and excretion of the top pediatric drugs were predicted. The current regulatory requirements and recommendations regarding safe and effective use of drugs in children are discussed. A few representative examples of the use of ontogeny-informed physiologically based pharmacokinetic (PBPK) models are highlighted. Expert opinion: Empirical prediction of pediatric drug dosing based on body weight or body-surface area from the adult parameters can be inaccurate because DMETs are not mature in children and the age-dependent maturation of these proteins is different. Ontogeny-informed-PBPK modeling provides a better alternative to predict the pharmacokinetics of drugs in children.

Disease states such as liver cirrhosis and chronic kidney disease can lead to altered pharmacokinetics (PK) of drugs by influencing drug absorption, blood flow to organs, plasma protein binding, apparent volume of distribution, and drug-metabolizing enzyme and transporter (DMET) abundance. Narrow therapeutic index drugs are particularly vulnerable to undesired pharmacodynamics (PD) because of the changes in drug PK in disease states. However, systematic clinical evaluation of disease effect on drug PK and PD is not always possible because of the complexity or the cost of clinical studies. Physiologically based PK (PBPK) modeling is emerging as an alternate method to extrapolate drug PK from the healthy population to disease states. These models require information on the effect of disease condition on the activity or tissue abundance of DMET proteins. Although immunoquantification-based abundance data were available in the literature for a limited number of DMET proteins, the emergence of mass spectrometry-based quantitative proteomics as a sensitive, robust, and high-throughput tool has allowed a rapid increase in data availability on tissue DMET abundance in healthy versus disease states, especially in liver tissue. Here, we summarize these data including the available immunoquantification or mRNA levels of DMET proteins (healthy vs disease states) in extrahepatic tissue and discuss the potential applications of DMET abundance data in enhancing the capability of PBPK modeling in predicting drug disposition across disease states. Successful examples of PBPK modeling that integrate differences in DMET proteins between healthy and disease states are discussed.

Furosemide is a widely used diuretic for treating excessive fluid accumulation caused by disease conditions like heart failure and liver cirrhosis. Furosemide tablet formulation exhibits variable pharmacokinetics (PK) with bioavailability ranging from 10 to almost 100%. To explain the variable absorption, we integrated the physicochemical, in vitro dissolution, permeability, distribution, and the elimination parameters of furosemide in a physiologically-based pharmacokinetic (PBPK) model. Although the intravenous PBPK model reasonably described the observed in vivo PK data, the reported low passive permeability failed to capture the observed data after oral administration. To mechanistically justify this discrepancy, we hypothesized that transporter-mediated uptake contributes to the oral absorption of furosemide in conjunction with passive permeability. Our in vitro results confirmed that furosemide is a substrate of intestinal breast cancer resistance protein (BCRP), multidrug resistance-associated protein 4 (MRP4), and organic anion transporting polypeptide 2B1 (OATP2B1), but it is not a substrate of P-glycoprotein (P-gp) and MRP2. We then estimated the net transporter-mediated intestinal uptake and integrated it into the PBPK model under both fasting and fed conditions. Our in vitro data and PBPK model suggest that the absorption of furosemide is permeability-limited, and OATP2B1 and MRP4 are important for its permeability across intestinal membrane. Further, as furosemide has been proposed as a probe substrate of renal organic anion transporters (OATs) for assessing clinical drug-drug interactions (DDIs) during drug development, the confounding effects of intestinal transporters identified in this study on furosemide PK should be considered in the clinical transporter DDI studies.

Population factors such as age, gender, ethnicity, genotype and disease state can cause inter-individual variability in pharmacokinetic (PK) profile of drugs. Primarily, this variability arises from differences in abundance of drug metabolizing enzymes and transporters (DMET) among individuals and/or groups. Hence, availability of compiled data on abundance of DMET proteins in different populations can be useful for developing physiologically based pharmacokinetic (PBPK) models. The latter are routinely employed for prediction of PK profiles and drug interactions during drug development and in case of special populations, where clinical studies either are not feasible or have ethical concerns. Therefore, the main aim of this work was to develop a repository of literature-reported DMET abundance data in various human tissues, which included compilation of information on sample size, technique(s) involved, and the demographic factors. The collation of literature reported data revealed high inter-laboratory variability in abundance of DMET proteins. We carried out unbiased meta-analysis to obtain weighted mean and percent coefficient of variation (%CV) values. The obtained %CV values were then integrated into a PBPK model to highlight the variability in drug PK in healthy adults, taking lamotrigine as a model drug. The validated PBPK model was extrapolated to predict PK of lamotrigine in paediatric and hepatic impaired populations. This study thus exemplifies importance of the DMET protein abundance database, and use of determined values of weighted mean and %CV after meta-analysis in PBPK modelling for the prediction of PK of drugs in healthy and special populations.

Cytosolic sulfotransferases (SULTs), including SULT1A, SULT1B, SULT1E, and SULT2A isoforms, play noteworthy roles in xenobiotic and endobiotic metabolism. We quantified the protein abundances of SULT1A1, SULT1A3, SULT1B1, and SULT2A1 in human liver cytosol samples (n = 194) by liquid chromatography-tandem mass spectrometry proteomics. The data were analyzed for their associations by age, sex, genotype, and ethnicity of the donors. SULT1A1, SULT1B1, and SULT2A1 showed significant age-dependent protein abundance, whereas SULT1A3 was invariable across 0-70 years. The respective mean abundances of SULT1A1, SULT1B1, and SULT2A1 in neonatal samples was 24%, 19%, and 38% of the adult levels. Interestingly, unlike UDP-glucuronosyltransferases and cytochrome P450 enzymes, SULT1A1 and SULT2A1 showed the highest abundance during early childhood (1 to <6 years), which gradually decreased by approx. 40% in adolescents and adults. SULT1A3 and SULT1B1 abundances were significantly lower in African Americans compared with Caucasians. Multiple linear regression analysis further confirmed the association of SULT abundances by age, ethnicity, and genotype. To demonstrate clinical application of the characteristic SULT ontogeny profiles, we developed and validated a proteomics-informed physiologically based pharmacokinetic model of acetaminophen. The latter confirmed the higher fractional contribution of sulfation over glucuronidation in the metabolism of acetaminophen in children. The study thus highlights that the ontogeny-based age-dependent fractional contribution (f(m)) of individual drug-metabolizing enzymes has better potential in prediction of drug-drug interactions and the effect of genetic polymorphisms in the pediatric population.