Hyaluronidase impacts exposures of long-acting injectable paliperidone palmitate in rodent models.

A significant challenge in the development of long-acting injectable drug formulations, especially for anti-infective agents, is delivering an efficacious dose within a tolerable injection volume. Co-administration of the extracellular matrix-degrading enzyme hyaluronidase can increase maximum tolerable injection volumes but is untested for this benefit with long-acting injectable formulations. One concern is that hyaluronidase could potentially alter the tissue response surrounding an injection depot, a response known to be important for drug release kinetics of long-acting injectable formulations. The objective of this pilot study was to evaluate the impact of co-administration of hyaluronidase on the drug release kinetics, pharmacokinetic profiles, and injection site histopathology of the long-acting injectable paliperidone palmitate for up to four weeks following intramuscular injection in mouse and rat models. In both species, co-administration of hyaluronidase increased paliperidone plasma exposures the first week after injection but did not negate the overall long-acting release nature of the formulation. Hyaluronidase-associated modification of the injection site depot was observed in mice but not in rats. These findings suggest that further investigation of hyaluronidase with long-acting injectable agents is warranted.

Complementarity of two proteomic data analysis tools in the identification of drug-metabolising enzymes and transporters in human liver.

Several software packages are available for the analysis of proteomic LC-MS/MS data, including commercial (e.g. Mascot/Progenesis LC-MS) and open access software (e.g. MaxQuant). In this study, Progenesis and MaxQuant were used to analyse the same data set from human liver microsomes (n = 23). Comparison focussed on the total number of peptides and proteins identified by the two packages. For the peptides exclusively identified by each software package, distribution of peptide length, hydrophobicity, molecular weight, isoelectric point and score were compared. Using standard cut-off peptide scores, we found an average of only 65% overlap in detected peptides, with surprisingly little consistency in the characteristics of peptides exclusively detected by each package. Generally, MaxQuant detected more peptides than Progenesis, and the additional peptides were longer and had relatively lower scores. Progenesis-specific peptides tended to be more hydrophilic and basic relative to peptides detected only by MaxQuant. At the protein level, we focussed on drug-metabolising enzymes (DMEs) and transporters, by comparing the number of unique peptides detected by the two packages for these specific proteins of interest, and their abundance. The abundance of DMEs and SLC transporters showed good correlation between the two software tools, but ABC showed less consistency. In conclusion, in order to maximise the use of MS datasets, we recommend processing with more than one software package. Together, Progenesis and MaxQuant provided excellent coverage, with a core of common peptides identified in a very robust way.

Arabidopsis root apical meristem survival during waterlogging is determined by phytoglobin through nitric oxide and auxin.

MAIN CONCLUSION: Over-expression of phytoglobin mitigates the degradation of the root apical meristem (RAM) caused by waterlogging through changes in nitric oxide and auxin distribution at the root tip. Plant performance to waterlogging is ameliorated by the over-expression of the Arabidopsis Phytoglobin 1 (Pgb1) which also contributes to the maintenance of a functional RAM. Hypoxia induces accumulation of ROS and damage in roots of wild type plants; these events were preceded by the exhaustion of the RAM resulting from the loss of functionality of the WOX5-expressing quiescent cells (QCs). These phenotypic deviations were exacerbated by suppression of Pgb1 and attenuated when the same gene was up-regulated. Genetic and pharmacological studies demonstrated that degradation of the RAM in hypoxic roots is attributed to a reduction in the auxin maximum at the root tip, necessary for the specification of the QC. This reduction was primarily caused by alterations in PIN-mediated auxin flow but not auxin synthesis. The expression and localization patterns of several PINs, including PIN1, 2, 3 and 4, facilitating the basipetal translocation of auxin and its distribution at the root tip, were altered in hypoxic WT and Pgb1-suppressing roots but mostly unchanged in those over-expressing Pgb1. Disruption of PIN1 and PIN2 signal in hypoxic roots suppressing Pgb1 initiated in the transition zone at 12 h and was specifically associated to the absence of Pgb1 protein in the same region. Exogenous auxin restored a functional RAM, while inhibition of the directional auxin flow exacerbated the degradation of the RAM. The regulation of root behavior by Pgb1 was mediated by nitric oxide (NO) in a model consistent with the recognized function of Pgbs as NO scavengers. Collectively, this study contributes to our understanding of the role of Pgbs in preserving root meristem function and QC niche during conditions of stress, and suggests that the root transition zone is most vulnerable to hypoxia.

Using Prior Knowledge on Systems Through PBPK to Gain Further Insight into Routine Clinical Data on Trough Concentrations: The Case of Tacrolimus in Chronic Kidney Disease.

BACKGROUND: Routine therapeutic drug monitoring (TDM) relies heavily on measuring trough drug concentrations. Trough concentrations are affected not only by drug bioavailability and clearance, but also by various patient and disease factors and the volume of distribution. This often makes interpreting differences in drug exposure from trough data challenging. This study aimed to combine the advantages of top-down analysis of therapeutic drug monitoring data with bottom-up physiologically-based pharmacokinetic (PBPK) modeling to investigate the effect of declining renal function in chronic kidney disease (CKD) on the nonrenal intrinsic metabolic clearance ( CLint ) of tacrolimus as a case example. METHODS: Data on biochemistry, demographics, and kidney function, along with 1167 tacrolimus trough concentrations for 40 renal transplant patients, were collected from the Salford Royal Hospital's database. A reduced PBPK model was developed to estimate CLint for each patient. Personalized unbound fractions, blood-to-plasma ratios, and drug affinities for various tissues were used as priors to estimate the apparent volume of distribution. Kidney function based on the estimated glomerular filtration rate ( eGFR ) was assessed as a covariate for CLint using the stochastic approximation of expectation and maximization method. RESULTS: At baseline, the median (interquartile range) eGFR was 45 (34.5-55.5) mL/min/1.73 m 2 . A significant but weak correlation was observed between tacrolimus CLint and eGFR (r = 0.2, P < 0.001). The CLint declined gradually (up to 36%) with CKD progression. Tacrolimus CLint did not differ significantly between stable and failing transplant patients. CONCLUSIONS: Kidney function deterioration in CKD can affect nonrenal CLint for drugs that undergo extensive hepatic metabolism, such as tacrolimus, with critical implications in clinical practice. This study demonstrates the advantages of combining prior system information (via PBPK) to investigate covariate effects in sparse real-world datasets.

Interplay between the Brassica napus phytoglobin (BnPgb1), folic acid, and antioxidant responses enhances plant tolerance to waterlogging.

Oxygen deprivation by waterlogging reduces the productivity of several crop species, including the oil-producing crop Brassica napus L., which is highly sensitive to excess moisture. Among factors induced by oxygen deficiency are phytoglobins (Pgbs), heme-containing proteins known to ameliorate the response of plants to the stress. This study examined the early responses to waterlogging in B. napus plants over-expressing or down-regulating the class 1 (BnPgb1) and class 2 (BnPgb2) Pgbs. The depression of gas exchange parameters and plant biomass was exacerbated by the suppression of BnPgb1, while suppression of BnPgb2 did not evoke any changes. This suggests that natural occurring levels of BnPgb1 (but not BnPg2) are required for the response of the plants to waterlogging. Typical waterlogging symptoms, including the accumulation of reactive oxygen species (ROS) and the deterioration of the root apical meristem (RAM) were attenuated by over-expression of BnPgb1. These effects were associated with the activation of antioxidant system and the transcriptional induction of folic acid (FA). Pharmacological treatments revealed that high levels of FA were sufficient to revert the inhibitory effect of waterlogging, suggesting that the interplay between BnPgb1, antioxidant responses and FA might contribute to plant tolerance to waterlogging stress.

Evaluating the safety and efficacy of the leukotriene receptor antagonist montelukast as adjuvant therapy in obese patients with type 2 diabetes mellitus: A double-blind, randomized, placebo-controlled trial.

Background: Type 2 diabetes mellitus (T2DM) is common with obesity. Metformin is a first-line therapy for this condition. However, it has only a minor impact on weight loss in some patients. Aim: This study aimed to evaluate the effectiveness, tolerability, and safety of combining montelukast therapy with metformin in obese diabetic patients. Methods: One hundred obese diabetic adult patients were recruited and randomized into two equal groups. Group 1 received placebo plus metformin 2 g/d, and Group 2 received 2 g/d metformin plus 10 mg/d montelukast. Demographic, anthropometric measurements (e.g., body weight, body mass index [BMI], and visceral adiposity index), lipid profile, diabetes control measures (fasting blood glucose, glycated hemoglobin [HbA1c], and homeostatic model assessment for insulin resistance [HOMA-IR]), adiponectin, and inflammatory markers (e.g., TNF-α, IL-6, and leukotriene B4) were assessed and reported for each group at baseline and after 12 weeks of treatment. Results: Both interventions significantly reduced all the measured parameters, except for adiponectin and HDL-C, levels of which increased compared to baseline data (p < 0.001). The montelukast group significantly improved in all parameters compared to the placebo group (ANCOVA test p < 0.001). The percentage changes in BMI, HbA1c, HOMA-IR, and inflammatory markers were 5%, 9%, 41%, and 5%-30%, respectively, in the placebo group compared to 8%, 16%, 58%, and 50%-70%, respectively, in the montelukast group. Conclusion: Montelukast adjuvant therapy was superior to metformin-only therapy in diabetes control and weight loss, most likely due to its increased insulin sensitivity and anti-inflammatory properties. The combination was tolerable and safe throughout the study duration. Clinical Trial Registration: [Clinicaltrial.gov], identifier [NCT04075110].

Pharmacokinetics under the COVID-19 storm.

AIMS: The storm-like nature of the health crises caused by COVID-19 has led to unconventional clinical trial practices such as the relaxation of exclusion criteria. The question remains: how can we conduct diverse trials without exposing subgroups of populations to potentially harmful drug exposure levels? The aim of this study was to build a knowledge base of the effect of intrinsic/extrinsic factors on the disposition of several repurposed COVID-19 drugs. METHODS: Physiologically based pharmacokinetic (PBPK) models were used to study the change in the pharmacokinetics (PK) of drugs repurposed for COVID-19 in geriatric patients, different race groups, organ impairment and drug-drug interactions (DDIs) risks. These models were also used to predict epithelial lining fluid (ELF) exposure, which is relevant for COVID-19 patients under elevated cytokine levels. RESULTS: The simulated PK profiles suggest no dose adjustments are required based on age and race for COVID-19 drugs, but dose adjustments may be warranted for COVID-19 patients also exhibiting hepatic/renal impairment. PBPK model simulations suggest ELF exposure to attain a target concentration was adequate for most drugs, except for hydroxychloroquine, azithromycin, atazanavir and lopinavir/ritonavir. CONCLUSION: We demonstrate that systematically collated data on absorption, distribution, metabolism and excretion, human PK parameters, DDIs and organ impairment can be used to verify simulated plasma and lung tissue exposure for drugs repurposed for COVID-19, justifying broader patient recruitment criteria. In addition, the PBPK model developed was used to study the effect of age and ethnicity on the PK of repurposed drugs, and to assess the correlation between lung exposure and relevant potency values from in vitro studies for SARS-CoV-2.

Randomized Clinical and Biochemical Study Comparing the Effect of L-arginine and Sildenafil in Beta Thalassemia Major Children With High Tricuspid Regurgitant Jet Velocity.

BACKGROUND: Pulmonary hypertension (PHT) is common in ß-thalassemia patients due to hemolysis, iron overload and diminished nitric oxide (NO) levels. Biochemical markers can help to understand the pathophysiology and to introduce new therapies for this condition. AIM: This study aimed to evaluate the effectiveness of L-arginine and sildenafil in thalassemia children with PHT at both clinical and biochemical levels. METHODS AND RESULTS: In a randomized controlled study, 60 ß-thalassemia major children with PHT were divided into 3 equal groups; Control group (Conventional thalassemia and PHT management), L-arginine group (Conventional + Oral L-arginine 0.1 mg.kg-1 daily), and sildenafil group (Conventional + Oral sildenafil 0.25 mg.kg-1 two times a day) for 60 days. Tricuspid Regurgitant Jet Velocity (TRJV) with Doppler echocardiography along with serum levels of NO, asymmetric dimethylarginine (ADMA), interleukin 1-beta (IL-1ß), E-selectin, and visfatin were followed-up at baseline, 30, and 60 days after treatment. Both drugs reduced the TRJV significantly. NO was significantly higher in both L-arginine and sildenafil groups after 60 days compared to baseline, while visfatin levels were lower. Only L-arginine reduced ADMA levels compared to baseline, while sildenafil did not. E-selectin and IL-1ß levels did not change remarkably by both drugs. NO and TRJV showed significant negative correlations in both treatment groups. CONCLUSION: L-arginine and sildenafil could clinically ameliorate chronic PHT whereas, L-arginine showed superiority to sildenafil on some biochemical markers.

Proteomic quantification of perturbation to pharmacokinetic target proteins in liver disease.

Model-based assessment of drug pharmacokinetics in liver disease requires quantification of abundance and disease-related changes in hepatic enzymes and transporters. This study aimed to assess performance of three label-free methods [high N (HiN), intensity-based absolute quantification (iBAQ) and total protein approach (TPA)] against QconCAT-based targeted data in healthy and diseased (cancer and cirrhosis) liver tissue. Measurements were compared across methods and disease-to-control ratios provided a 'disease perturbation factor' (DPF) for each protein. Mean label-free measurements of targets correlated well (Pearson's coefficient, r = 0.91-0.98 p < 0.001) and with targeted data (r = 0.65-0.95, p < 0.001). Concordance with targeted data was generally moderate (Lin's concordance coefficient, ρc = 0.46-0.92), depending on methodology. Moderate precision and accuracy were observed for label-free methods (average fold error, AFE = 1.44-1.68; absolute average fold error, AAFE = 2.44-3.23). The DPF reconciled the data and indicated downregulated expression in cancer and cirrhosis, consistent with an inflammatory effect. HiN estimated perturbation consistently with targeted data (AFEHiN = 1.07, AAFEHiN = 1.57), whereas iBAQ overestimated (AFEiBAQ = 0.81, AAFEiBAQ = 1.67) and TPA underestimated (AFETPA = 1.37, AAFETPA = 1.65) disease effect. Progression from mild to severe cirrhosis was consistent with progressive decline in expression, reproduced by HiN but overestimated by iBAQ and underestimated by TPA (AFEHiN = 0.98, AFEiBAQ = 0.60, AFETPA = 1.24). DPF data confirmed non-uniform disease effect on drug-elimination pathways and progressive impact of disease severity. SIGNIFICANCE: This study demonstrated good correlation and moderate concordance between intensity-based label-free proteomic methods (HiN, iBAQ and TPA) and targeted data. Label-free measurements tended to overestimate abundance, but differences were reconciled using a disease perturbation factor (DPF) for each protein. With targeted data as a reference, HiN defined disease perturbation and the impact of disease progression consistently, indicating that the use of 'razor' peptides for quantification against an exogenous standard provides biologically sensible quantitative fingerprints of disease. Disease-driven perturbations in expression relative to healthy baseline are incorporated into drug kinetic models used to predict drug exposure in disease populations where clinical studies may not be feasible.

A family of QconCATs (Quantification conCATemers) for the quantification of human pharmacological target proteins.

We have developed a family of QconCAT standards for the absolute quantification of pharmacological target proteins in a variety of human tissues. The QconCATs consist of concatenated proteotypic peptides, are designed in silico, and expressed in E. coli in media enriched with [13C6] arginine and [13C6] lysine to generate stable isotope-labeled multiplexed absolute quantification standards. The so-called MetCAT (used to quantify cytochrome P450 (CYP) and glucuronosyltransferase (UGT) enzymes), the liver TransCAT (used to quantify plasma-membrane drug transporters) and the brain TransCAT (used to quantify transporters expressed in the blood-brain barrier) were previously reported. We now report new QconCATs for the quantification of non-UGT non-CYP drug metabolizing enzymes (NuncCAT) and receptor tyrosine kinases (KinCAT). We have also redesigned the liver TransCAT, replacing problematic peptides and the N-terminal tag, for better characterization and expression. All these QconCATs showed high purity, high labelling efficiency with stable 13C isotope (>95%), and high sequence coverage (>88%). They represent a close-knit family of standards for quantifying pharmacokinetic targets, together with a more distant cousin, the KinCAT, used to quantify pharmacodynamic targets. SIGNIFICANCE: Multiplexed determination of absolute protein abundances using quantitative conCATemers (QconCATs) has already been successfully demonstrated in different human tissues. We have previously reported two QconCATs; MetCAT and TransCAT, for the quantification of key enzymes (cytochrome P450 enzymes (CYP) and glucuronosyltransferases (UGT)) and drug transporters. To build on these reports, application of the QconCAT methodology for the determination of non-UGT non-CYP enzymes and receptor tyrosine kinases (RTKs) in human tissue is reported here. This report focuses on development and characterization of two QconCAT constructs for the quantification of 24 enzymes and 21 RTKs. We demonstrate that the developed QconCATs have high purity, high incorporation efficiency and low peptide miscleavage upon proteolysis. Application of these QconCATs for reliable quantification of target proteins was achieved in human liver.

Mining of Leaf Rust Resistance Genes Content in Egyptian Bread Wheat Collection.

Wheat is a major nutritional cereal crop that has economic and strategic value worldwide. The sustainability of this extraordinary crop is facing critical challenges globally, particularly leaf rust disease, which causes endless problems for wheat farmers and countries and negatively affects humanity's food security. Developing effective marker-assisted selection programs for leaf rust resistance in wheat mainly depends on the availability of deep mining of resistance genes within the germplasm collections. This is the first study that evaluated the leaf rust resistance of 50 Egyptian wheat varieties at the adult plant stage for two successive seasons and identified the absence/presence of 28 leaf rust resistance (Lr) genes within the studied wheat collection. The field evaluation results indicated that most of these varieties demonstrated high to moderate leaf rust resistance levels except Gemmeiza 1, Gemmeiza 9, Giza162, Giza 163, Giza 164, Giza 165, Sids 1, Sids 2, Sids 3, Sakha 62, Sakha 69, Sohag 3 and Bany Swif 4, which showed fast rusting behavior. On the other hand, out of these 28 Lr genes tested against the wheat collection, 21 Lr genes were successfully identified. Out of 15 Lr genes reported conferring the adult plant resistant or slow rusting behavior in wheat, only five genes (Lr13, Lr22a, Lr34, Lr37, and Lr67) were detected within the Egyptian collection. Remarkedly, the genes Lr13, Lr19, Lr20, Lr22a, Lr28, Lr29, Lr32, Lr34, Lr36, Lr47, and Lr60, were found to be the most predominant Lr genes across the 50 Egyptian wheat varieties. The molecular phylogeny results also inferred the same classification of field evaluation, through grouping genotypes characterized by high to moderate leaf rust resistance in one cluster while being highly susceptible in a separate cluster, with few exceptions.

Non-uniformity of Changes in Drug-Metabolizing Enzymes and Transporters in Liver Cirrhosis: Implications for Drug Dosage Adjustment.

Liver cirrhosis is a chronic disease that affects the liver structure, protein expression, and overall metabolic function. Abundance data for drug-metabolizing enzymes and transporters (DMET) across all stages of disease severity are scarce. Levels of these proteins are crucial for the accurate prediction of drug clearance in hepatically impaired patients using physiologically based pharmacokinetic (PBPK) models, which can be used to guide the selection of more precise dosing. This study aimed to experimentally quantify these proteins in human liver samples and assess how they can impact the predictive performance of the PBPK models. We determined the absolute abundance of 51 DMET proteins in human liver microsomes across the three degrees of cirrhosis severity (n = 32; 6 mild, 13 moderate, and 13 severe), compared to histologically normal controls (n = 14), using QconCAT-based targeted proteomics. The results revealed a significant but non-uniform reduction in the abundance of enzymes and transporters, from control, by 30-50% in mild, 40-70% in moderate, and 50-90% in severe cirrhosis groups. Cancer and/or non-alcoholic fatty liver disease-related cirrhosis showed larger deterioration in levels of CYP3A4, 2C8, 2E1, 1A6, UGT2B4/7, CES1, FMO3/5, EPHX1, MGST1/3, BSEP, and OATP2B1 than the cholestasis set. Drug-specific pathways together with non-uniform changes of abundance across the enzymes and transporters under various degrees of cirrhosis necessitate the use of PBPK models. As case examples, such models for repaglinide, dabigatran, and zidovudine were successful in recovering disease-related alterations in drug exposure. In conclusion, the current study provides the biological rationale behind the absence of a single dose adjustment formula for all drugs in cirrhosis and demonstrates the utility of proteomics-informed PBPK modeling for drug-specific dose adjustment in liver cirrhosis.

Review article: time to revisit Child-Pugh score as the basis for predicting drug clearance in hepatic impairment.

BACKGROUND: Prescription information for many drugs entering the market lacks dosage guidance for hepatic impairment. Dedicated studies for assessing the fate of drugs in hepatic impairment commonly stratify patients using Child-Pugh score. Child-Pugh is a prognostic clinical score with limitations in reflecting the liver's metabolic capacity. AIMS: To demonstrate the need for better drug dosing approaches in hepatic impairment, summarise the current status, identify knowledge gaps related to drug kinetic parameters in hepatic impairment, propose solutions for predicting the liver disease impact on drug exposure and discuss barriers to dosing guidance in those patients. METHODS: Relevant reports on dosage adjustment in hepatic impairment were analysed concerning the prediction of the impairment impact on drug kinetics using physiologically-based pharmacokinetic (PBPK) modelling. RESULTS: PBPK models are suggested as a potential framework to understand drug clearance changes in hepatic impairment. Quantifying changes in abundance and activity of drug-metabolising enzymes and transporters, understanding the impact of shunting, and accounting for interindividual variations in drug absorption could help in extending the success of these models in hepatically-impaired populations. These variables might not correlate with Child-Pugh score as a whole. Therefore, new metabolic activity markers, imaging techniques and other scoring systems are proposed to either support or substitute Child-Pugh score. CONCLUSIONS: Many physiological changes in hepatic impairment determining the fate of drugs do not necessarily correlate with Child-Pugh score. Quantifying these changes in individual patients is essential in future hepatic impairment studies. Further studies assessing Child-Pugh alternatives are recommended to allow better prediction of drug exposure.

Bioanalytical strategies in drug discovery and development.

A reliable, rapid, and effective bioanalytical method is essential for the determination of the pharmacokinetic, pharmacodynamic, and toxicokinetic parameters that inform the safety and efficacy profile of investigational drugs. The overall goal of bioanalytical method development is to elucidate the procedure and operating conditions under which a method can sufficiently extract, qualify, and/or quantify the analyte(s) of interest and/or their metabolites for the intended purpose. Given the difference in the physicochemical properties of small and large molecule drugs, different strategies need to be adopted for the development of an effective and efficient bioanalytical method. Herein, we provide an overview of different sample preparation strategies, analytical platforms, as well as procedures for achieving high throughput for bioanalysis of small and large molecule drugs.

Proteomic Quantification of Changes in Abundance of Drug-Metabolizing Enzymes and Drug Transporters in Human Liver Cirrhosis: Different Methods, Similar Outcomes.

Model-based assessment of the effects of liver disease on drug pharmacokinetics requires quantification of changes in enzymes and transporters responsible for drug metabolism and disposition. Different proteomic methods are currently used for protein quantification in tissues and in vitro systems, each with specific procedures and requirements. The outcome of quantitative proteomic assays using four different methods (one targeted and three label-free) applied to the same sample set was compared in this study. Three pooled cirrhotic liver microsomal samples corresponding to cirrhosis with nonalcoholic fatty liver disease, biliary disease, or cancer and a control microsomal pool were analyzed using quantification concatemer-based targeted proteomics, the total protein approach (TPA), high three ion intensity (Hi3) approach, and intensity-based absolute quantification (iBAQ) to determine the absolute and relative abundance in disease compared with control. The relative abundance data provided a "disease perturbation factor" (DPF) for each target protein. Absolute and relative abundances generated by standard-based label-free methods (iBAQ and Hi3) showed good agreement with targeted proteomics (limited bias and scatter), but TPA (standard-free method) overestimated absolute abundances by approximately 2-fold. The DPF was consistent between different proteomic methods but varied between enzymes and transporters, indicating discordance of effects of cirrhosis on various metabolism-related proteins. The DPF ranged from no change (e.g., for glucuronosyltransferase-1A6 in nonalcoholic fatty liver disease group) to less than 0.3 (e.g., carboxylesterases-1 in cirrhosis of biliary origin). SIGNIFICANCE STATEMENT: This study demonstrated that relative changes in enzymes and transporters (DPF) are independent of the quantitative proteomic methods used. Standard-based label-free methods, such as high three ion intensity (Hi3) and intensity-based absolute quantification (iBAQ) methods, were less biased and more precise than the total protein approach (TPA) when compared with targeted data. The DPF reconciled differences across proteomic methods observed with absolute levels. Using this approach, differences were revealed in the expression of enzymes/transporters in cirrhosis associated with different etiologies.

Physiological-based pharmacokinetic modeling trends in pharmaceutical drug development over the last 20-years; in-depth analysis of applications, organizations, and platforms.

We assess the advancement of physiologically based pharmacokinetic (PBPK) modeling and simulation (M&S) over the last 20 years (start of 2000 to end of 2019) focusing on the trends in each decade with the relative contributions from different organizations, areas of applications, and software tools used. Unlike many of the previous publications which focused on regulatory applications, our analysis is based on PBPK publications in peer-reviewed journals based on a large sample (>700 original articles). We estimated a rate of growth for PBPK (>40 fold/20 years) that was much steeper than the general pharmacokinetic modeling (<3 fold/20 years) or overall scientific publications (∼3 fold/20 years). The analyses demonstrated that contrary to commonly held belief, commercial specialized PBPK platforms with graphical-user interface were a much more popular choice than open-source alternatives even within academic organizations. These platforms constituted 81% of the whole set of the sample we assessed. The major PBPK applications (top 3) were associated with the study design, predicting formulation effects, and metabolic drug-drug interactions, while studying the fate of drugs in special populations, predicting kinetics in early drug development, and investigating transporter drug interactions have increased proportionally over the last decade. The proportions of application areas based on published research were distinctively different from those shown previously for the regulatory submissions and impact on labels. This may demonstrate the lag time between the research applications versus verified usage within the regulatory framework. The report showed the trend of overall PBPK publications in pharmacology drug development from the past 2 decades stratified by the organizations involved, software used, and area of applications. The analysis showed a more rapid increase in PBPK than that of the pharmacokinetic space itself with an equal contribution from academia and industry. By establishing and recording the journey of PBPK modeling in the past and looking at its current status, the analysis can be used for devising plans based on the anticipated trajectory of future regulatory applications.

Mass spectrometry-based abundance atlas of ABC transporters in human liver, gut, kidney, brain and skin.

ABC transporters (ATP-binding cassette transporter) traffic drugs and their metabolites across membranes, making ABC transporter expression levels a key factor regulating local drug concentrations in different tissues and individuals. Yet, quantification of ABC transporters remains challenging because they are large and low-abundance transmembrane proteins. Here, we analysed 200 samples of crude and membrane-enriched fractions from human liver, kidney, intestine, brain microvessels and skin, by label-free quantitative mass spectrometry. We identified 32 (out of 48) ABC transporters: ABCD3 was the most abundant in liver, whereas ABCA8, ABCB2/TAP1 and ABCE1 were detected in all tissues. Interestingly, this atlas unveiled that ABCB2/TAP1 may have TAP2-independent functions in the brain and that biliary atresia (BA) and control livers have quite different ABC transporter profiles. We propose that meaningful biological information can be derived from a direct comparison of these data sets.

Scaling Factors for Clearance in Adult Liver Cirrhosis.

In vitro to in vivo extrapolation (IVIVE) enables prediction of in vivo clinical outcomes related to drug exposure in various populations from in vitro data. Prudent IVIVE requires scalars specific to the biologic characteristics of the system in each population. This study determined experimentally for the first time scalars in liver samples from patients with varying degrees of cirrhosis. Microsomal and cytosolic fractions were extracted from 13 noncirrhotic and 32 cirrhotic livers (six mild, 13 moderate, and 13 severe, based on Child-Pugh score). Fractional protein content was determined, and cytochrome P450 reductase activity was used to correct for microsomal protein loss. Although the median microsomal protein per gram liver (MPPGL) in mild, moderate, and severe cirrhosis (26.2, 32.4, and 30.8 mg⋅g-1, respectively) seemed lower than control livers (36.6 mg⋅g-1), differences were not statistically significant (Kruskal-Wallis test, P > 0.05). Corresponding values for cytosolic protein per gram liver were 88.2, 67.9, 62.2, and 75.4 (mg⋅g-1) for mild, moderate, and severe cirrhosis and control livers, respectively, with statistically lower values for severe versus controls (Mann-Whitney P = 0.006). Cirrhosis associated with cancer showed lower MPPGL (24.8 mg⋅g-1) than cirrhosis associated with cholestasis (38.3 mg⋅g-1, P = 0.003). Physiologically based pharmacokinetic simulations with disease-specific scalars captured cirrhosis impact on exposure to alfentanil, metoprolol, midazolam, and ethinylestradiol. These experimentally-determined scalars should alleviate the need for indirect scaling using functional liver volume. Scaling factors in cirrhosis might be a reflection of the etiology rather than the disease severity. Hence, bundling various cirrhotic conditions under the same umbrella when predicting hepatic impairment impact should be revisited. SIGNIFICANCE STATEMENT: Cirrhosis-specific scalars required for extrapolation from microsomal or cytosolic in vitro systems to liver tissue are lacking. These scalars can help in predicting drug clearance and selection of dosage regimens for cirrhosis populations. Attempts to consider potential changes have been empirical and ignored the potential impact of the cause of cirrhosis. We obtained experimental values for these scalars for the first time and assessed their impact on predicted exposure to various substrate drugs using physiologically-based pharmacokinetics simulations.

Stem cell fate in hypoxic root apical meristems is influenced by phytoglobin expression.

Root survival to flooding-induced hypoxic stress is dependent upon maintaining the functionality of the root apical meristem quiescent center (QC), a process that is governed by the basipetal flow of auxin leading to the formation of an auxin maximum, which is needed for the establishment of a highly oxidized environment specifying the QC niche. Perturbations in auxin flow and distribution along the root profile occurring during hypoxia can shift the redox state of the QC towards a more reduced environment, leading to the activation of the QC, degradation of the meristem, and root abortion. The maize phytoglobin gene ZmPgb1.1 is involved in minimizing these damaging effects during hypoxia in processes that result in sustaining the PIN-mediated auxin maximum and an oxidized environment in the QC. The oxidized environment is accomplished by maintaining the activity of redox enzymes oxidizing ascorbate and glutathione. These events, compromised in QCs suppressing ZmPgb1.1, ensure the functionality of the QC and root meristems under conditions of low oxygen, resulting in stable root performance.

Quantitative mass spectrometry-based proteomics in the era of model-informed drug development: Applications in translational pharmacology and recommendations for best practice.

Quantitative translation of the fate and action of a drug in the body is facilitated by models that allow extrapolation of in vitro measurements (such as the rate of metabolism, active transport across membranes, inhibition of enzymes and receptor occupancy) to in vivo consequences (intensity and duration of drug effects). These models use various physiological parameters, including data that describe the expression levels of pharmacologically relevant enzymes, transporters and receptors in tissues and in vitro systems. Immunoquantification approaches have traditionally been used to determine protein expression levels, generally providing relative quantification data with compromised selectivity and reproducibility. More recently, the development of several quantitative proteomic techniques, fuelled by advances in state-of-the-art mass spectrometry, has led to generating a wealth of qualitative and quantitative data. These data are currently used for various quantitative systems pharmacology applications, with the ultimate goal of conducting virtual clinical trials to inform clinical studies, especially when assessments are difficult to conduct on patients. In this review, we explore available quantitative proteomic methods, discuss their main applications in translational pharmacology and offer recommendations for selecting and implementing proteomic techniques.