The CGG repeat expansion in RILPL1 is associated with oculopharyngodistal myopathy type 4.

Recent studies indicate that CGG repeat expansions in LRP12, GIPC1, and NOTCH2NLC are associated with oculopharyngodistal myopathy (OPDM) types 1, 2, and 3, respectively. However, some clinicopathologically confirmed OPDM cases continue to have unknown genetic causes. Here, through a combination of long-read whole-genome sequencing (LRS), repeat-primed polymerase chain reaction (RP-PCR), and fluorescence amplicon length analysis PCR (AL-PCR), we found that a CGG repeat expansion in the 5' UTR of RILPL1 is associated with familial and simplex OPDM type 4 (OPDM4). The number of repeats ranged from 139 to 197. Methylation analysis indicates that the methylation levels in RILPL1 were unaltered in OPDM4 individuals. Analyses of muscle biopsies suggested that the expanded CGG repeat might be translated into a toxic poly-glycine protein that co-localizes with p62 in intranuclear inclusions. Moreover, analyses suggest that the toxic RNA gain-of-function effects also contributed to the pathogenesis of this disease. Intriguingly, all four types of OPDM have been found to be associated with the CGG repeat expansions located in 5' UTRs. This finding suggests that a common pathogenic mechanism, driven by the CGG repeat expansion, might underlie all cases of OPDM.

Non-coding CGG repeat expansion in LOC642361/NUTM2B-AS1 is associated with a phenotype of oculopharyngodistal myopathy.

BACKGROUND: Oculopharyngodistal myopathy (OPDM) is a rare adult-onset neuromuscular disease, associated with CGG repeat expansions in the 5' untranslated region of LRP12, GIPC1, NOTCH2NLC and RILPL1. However, the genetic cause of a proportion of pathoclinically confirmed cases remains unknown. METHODS: A total of 26 OPDM patients with unknown genetic cause(s) from 4 tertiary referral hospitals were included in this study. Clinical data and laboratory findings were collected. Muscle samples were observed by histological and immunofluorescent staining. Long-read sequencing was initially conducted in six patients with OPDM. Repeat-primed PCR was used to screen the CGG repeat expansions in LOC642361/NUTM2B-AS1 in all 26 patients. RESULTS: We identified CGG repeat expansion in the non-coding transcripts of LOC642361/NUTM2B-AS1 in another two unrelated Chinese cases with typical pathoclinical features of OPDM. The repeat expansion was more than 70 times in the patients but less than 40 times in the normal controls. Both patients showed no leucoencephalopathy but one showed mild cognitive impairment detected by Montreal Cognitive Assessment. Rimmed vacuoles and p62-positive intranuclear inclusions (INIs) were identified in muscle pathology, and colocalisation of CGG RNA foci with p62 was also found in the INIs of patient-derived fibroblasts. CONCLUSIONS: We identified another two unrelated cases with CGG repeat expansion in the long non-coding RNA of the LOC642361/NUTM2B-AS1 gene, presenting with a phenotype of OPDM. Our cases broadened the recognised phenotypic spectrum and pathogenesis in the disease associated with CGG repeat expansion in LOC642361/NUTM2B-AS1.

A synonymous codon variant altering splicing of RBCK1 expands the phenotype and genotype spectra of polyglucosan body myopathy 1.

Polyglucosan body myopathy type 1 (PGBM1, OMIM #615895.) is a rare autosomal recessive disorder caused by RBCK1 mutations. The patients displayed polyglucosan accumulation in skeletal and cardiac muscles, giving rise to loss of ambulation and heart failure with or without immune system dysregulation. So far, only 24 patients have been reported, all of whom exhibited symptoms before adulthood. Here, we reported the first case of an adult-onset PGBM1 patient with a novel compound heterozygous RBCK1 gene mutation consisting of a nonsense and synonymous variant affecting splicing.

The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance.

Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CLint) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.

Physiologically-Based Pharmacokinetic Modeling of PAXLOVID™ with First-Order Absorption Kinetics.

PURPOSE: PAXLOVID™ is nirmatrelvir tablets co-packaged with ritonavir tablets. Ritonavir is used as a pharmacokinetics (PK) enhancer to reduce metabolism and increase exposure of nirmatrelvir. This is the first disclosure of Paxlovid physiologically-based pharmacokinetic (PBPK) model. METHODS: Nirmatrelvir PBPK model with first-order absorption kinetics was developed using in vitro, preclinical, and clinical data of nirmatrelvir in the presence and absence of ritonavir. Clearance and volume of distribution were derived from nirmatrelvir PK obtained using a spray-dried dispersion (SDD) formulation where it is considered to be dosed as an oral solution, and absorption is near complete. The fraction of nirmatrelvir metabolized by CYP3A was estimated based on in vitro and clinical ritonavir drug-drug interaction (DDI) data. First-order absorption parameters were established for both SDD and tablet formulation using clinical data. Nirmatrelvir PBPK model was verified with both single and multiple dose human PK data, as well as DDI studies. Simcyp® first-order ritonavir compound file was also verified with additional clinical data. RESULTS: The nirmatrelvir PBPK model described the observed PK profiles of nirmatrelvir well with predicted AUC and Cmax values within ± 20% of the observed. The ritonavir model performed well resulting in predicted values within twofold of observed. CONCLUSIONS: Paxlovid PBPK model developed in this study can be applied to predict PK changes in special populations, as well as model the effect of victim and perpetrator DDI. PBPK modeling continues to play a critical role in accelerating drug discovery and development of potential treatments for devastating diseases such as COVID-19. NCT05263895, NCT05129475, NCT05032950 and NCT05064800.

Species differences in plasma protein binding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease inhibitor nirmatrelvir.

Plasma protein binding (PPB) studies on the SARS-CoV-2 main protease inhibitor nirmatrelvir revealed considerable species differences primarily in dog and rabbit, which prompted further investigations into the biochemical basis for these differences.The unbound fraction (fu) of nirmatrelvir in dog and rabbit plasma was concentration (2-200 µM)-dependent (dog fu,p 0.024-0.69, rabbit fu,p 0.010-0.82). Concentration (0.1-100 µM)-dependent binding in serum albumin (SA) (fu,SA 0.040-0.82) and alpha-1-acid glycoprotein (AAG) (fu,AAG 0.050-0.64) was observed in dogs. Nirmatrelvir showed minimal binding to rabbit SA (1-100 µM: fu,SA 0.70-0.79), while binding to rabbit AAG was concentration-dependent (0.1-100 µM: fu,AAG 0.024-0.66). In contrast, nirmatrelvir (2 µM) revealed minimal binding (fu,AAG 0.79-0.88) to AAG from rat and monkeys. Nirmatrelvir showed minimal-to-moderate binding to SA (1-100 µM; fu,SA 0.70-1.0) and AAG (0.1-100 µM; fu,AAG 0.48-0.58) from humans across tested concentrations.Nirmatrelvir molecular docking studies using published crystal structures and homology models of human and preclinical species SA and AAG were used to rationalise the species differences to plasma proteins. This suggested that species differences in PPB are primarily driven by molecular differences in albumin and AAG resulting in differences in binding affinity.

Glycogen synthesis and beyond, a comprehensive review of GSK3 as a key regulator of metabolic pathways and a therapeutic target for treating metabolic diseases.

Glycogen synthase kinase-3 (GSK3) is a highly evolutionarily conserved serine/threonine protein kinase first identified as an enzyme that regulates glycogen synthase (GS) in response to insulin stimulation, which involves GSK3 regulation of glucose metabolism and energy homeostasis. Both isoforms of GSK3, GSK3α, and GSK3ß, have been implicated in many biological and pathophysiological processes. The various functions of GSK3 are indicated by its widespread distribution in multiple cell types and tissues. The studies of GSK3 activity using animal models and the observed effects of GSK3-specific inhibitors provide more insights into the roles of GSK3 in regulating energy metabolism and homeostasis. The cross-talk between GSK3 and some important energy regulators and sensors and the regulation of GSK3 in mitochondrial activity and component function further highlight the molecular mechanisms in which GSK3 is involved to regulate the metabolic activity, beyond its classical regulatory effect on GS. In this review, we summarize the specific roles of GSK3 in energy metabolism regulation in tissues that are tightly associated with energy metabolism and the functions of GSK3 in the development of metabolic disorders. We also address the impacts of GSK3 on the regulation of mitochondrial function, activity and associated metabolic regulation. The application of GSK3 inhibitors in clinical tests will be highlighted too. Interactions between GSK3 and important energy regulators and GSK3-mediated responses to different stresses that are related to metabolism are described to provide a brief overview of previously less-appreciated biological functions of GSK3 in energy metabolism and associated diseases through its regulation of GS and other functions.

CT-based radiogenomic analysis dissects intratumor heterogeneity and predicts prognosis of colorectal cancer: a multi-institutional retrospective study.

BACKGROUND: This study aimed to develop a radiogenomic prognostic prediction model for colorectal cancer (CRC) by investigating the biological and clinical relevance of intratumoural heterogeneity. METHODS: This retrospective multi-cohort study was conducted in three steps. First, we identified genomic subclones using unsupervised deconvolution analysis. Second, we established radiogenomic signatures to link radiomic features with prognostic subclone compositions in an independent radiogenomic dataset containing matched imaging and gene expression data. Finally, the prognostic value of the identified radiogenomic signatures was validated using two testing datasets containing imaging and survival information collected from separate medical centres. RESULTS: This multi-institutional retrospective study included 1601 patients (714 females and 887 males; mean age, 65 years ± 14 [standard deviation]) with CRC from 5 datasets. Molecular heterogeneity was identified using unsupervised deconvolution analysis of gene expression data. The relative prevalence of the two subclones associated with cell cycle and extracellular matrix pathways identified patients with significantly different survival outcomes. A radiogenomic signature-based predictive model significantly stratified patients into high- and low-risk groups with disparate disease-free survival (HR = 1.74, P = 0.003). Radiogenomic signatures were revealed as an independent predictive factor for CRC by multivariable analysis (HR = 1.59, 95% CI:1.03-2.45, P = 0.034). Functional analysis demonstrated that the 11 radiogenomic signatures were predominantly associated with extracellular matrix and immune-related pathways. CONCLUSIONS: The identified radiogenomic signatures might be a surrogate for genomic signatures and could complement the current prognostic strategies.

Transporter-Enzyme Interplay in the Pharmacokinetics of PF-06835919, A First-in-class Ketohexokinase Inhibitor for Metabolic Disorders and Non-alcoholic Fatty Liver Disease.

Excess dietary fructose consumption promotes metabolic dysfunction thereby increasing the risk of obesity, type 2 diabetes, non-alcoholic steatohepatitis (NASH), and related comorbidities. PF-06835919, a first-in-class ketohexokinase (KHK) inhibitor, showed reversal of such metabolic disorders in preclinical models and clinical studies, and is under clinical development for the potential treatment of NASH. In this study, we evaluated the transport and metabolic pathways of PF-06835919 disposition and assessed pharmacokinetics in preclinical models. PF-06835919 showed active uptake in cultured primary human hepatocytes, and substrate activity to organic anion transporter (OAT)2 and organic anion transporting-polypeptide (OATP)1B1 in transfected cells. "SLC-phenotyping" studies in human hepatocytes suggested contribution of passive uptake, OAT2- and OATP1B-mediated transport to the overall uptake to be about 15%, 60% and 25%, respectively. PF-06835919 showed low intrinsic metabolic clearance in vitro, and was found to be metabolized via both oxidative pathways (58%) and acyl glucuronidation (42%) by CYP3A, CYP2C8, CYP2C9 and UGT2B7. Following intravenous dosing, PF-06835919 showed low clearance (0.4-1.3 mL/min/kg) and volume of distribution (0.17-0.38 L/kg) in rat, dog and monkey. Human oral pharmacokinetics are predicted within 20% error when considering transporter-enzyme interplay in a PBPK model. Finally, unbound liver-to-plasma ratio (Kpuu) measured in vitro using rat, NHP and human hepatocytes was found to be approximately 4, 25 and 10, respectively. Similarly, liver Kpuu in rat and monkey following intravenous dosing of PF-06835919 was found to be 2.5 and 15, respectively, and notably higher than the muscle and brain Kpuu, consistent with the active uptake mechanisms observed in vitro. Significance Statement This work characterizes the transport/metabolic pathways in the hepatic disposition of PF-06835919, a first-in-class KHK inhibitor for the treatment of metabolic disorders and NASH. Phenotyping studies using transfected systems, human hepatocytes and liver microsomes signifies the role of OAT2 and OATP1B1 in the hepatic uptake and multiple enzymes in the metabolism of PF-06835919. Data presented suggest hepatic transporter-enzyme interplay in determining its systemic concentrations and potential enrichment in liver, a target site for KHK inhibition.

Disposition of Nirmatrelvir, an Orally Bioavailable Inhibitor of SARS-CoV-2 3C-Like Protease, across Animals and Humans.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 3C-like protease inhibitor PF-07321332 (nirmatrelvir), in combination with ritonavir (Paxlovid), was recently granted emergency use authorization by multiple regulatory agencies for the treatment of coronavirus disease 2019 (COVID-19) in adults and pediatric patients. Disposition studies on nirmatrelvir in animals and in human reagents, which were used to support clinical studies, are described herein. Plasma clearance was moderate in rats (27.2 ml/min per kg) and monkeys (17.1 ml/min per kg), resulting in half-lives of 5.1 and 0.8 hours, respectively. The corresponding oral bioavailability was moderate in rats (34%-50%) and low in monkeys (8.5%), primarily due to oxidative metabolism along the gastrointestinal tract in this species. Nirmatrelvir demonstrated moderate plasma protein binding in rats, monkeys, and humans with mean unbound fractions ranging from 0.310 to 0.478. The metabolism of nirmatrelvir was qualitatively similar in liver microsomes and hepatocytes from rats, monkeys, and humans; prominent metabolites arose via cytochrome P450 (CYP450)-mediated oxidations on the P1 pyrrolidinone ring, P2 6,6-dimethyl-3-azabicyclo[3.1.0]hexane, and the tertiary-butyl group at the P3 position. Reaction phenotyping studies in human liver microsomes revealed that CYP3A4 was primarily responsible (fraction metabolized = 0.99) for the oxidative metabolism of nirmatrelvir. Minor clearance mechanisms involving renal and biliary excretion of unchanged nirmatrelvir were also noted in animals and in sandwich-cultured human hepatocytes. Nirmatrelvir was a reversible and time-dependent inhibitor as well as inducer of CYP3A activity in vitro. First-in-human pharmacokinetic studies have demonstrated a considerable boost in the oral systemic exposure of nirmatrelvir upon coadministration with the CYP3A4 inhibitor ritonavir, consistent with the predominant role of CYP3A4 in nirmatrelvir metabolism. SIGNIFICANCE STATEMENT: The manuscript describes the preclinical disposition, metabolism, and drug-drug interaction potential of PF-07321332 (nirmatrelvir), an orally active peptidomimetic-based inhibitor of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 3CL protease, which has been granted emergency use authorization by multiple regulatory agencies around the globe for the treatment of coronavirus disease 2019 (COVID-19) in COVID-19-positive adults and pediatric patients who are at high risk for progression to severe COVID-19, including hospitalization or death.

In Vitro - in Vivo Extrapolation of Hepatic Clearance in Preclinical Species.

Accurate prediction of human clearance is of critical importance in drug discovery. In this study, in vitro - in vivo extrapolation (IVIVE) of hepatic clearance was established using large sets of compounds for four preclinical species (mouse, rat, dog, and non-human primate) to enable better understanding of clearance mechanisms and human translation. In vitro intrinsic clearances were obtained using pooled liver microsomes (LMs) or hepatocytes (HEPs) and scaled to hepatic clearance using the parallel-tube and well-stirred models. Subsequently, IVIVE scaling factors (SFs) were derived to best predict in vivo clearance. The SFs for extended clearance classification system (ECCS) class 2/4 compounds, involving metabolic clearance, were generally small (≤ 2.6) using both LMs and HEPs with parallel-tube model, with the exception of the rodents (~ 2.4-4.6), suggesting in vitro reagents represent in vivo reasonably well. SFs for ECCS class 1A and 1B are generally higher than class 2/4 across the species, likely due to the contribution of transporter-mediated clearance that is under-represented with in vitro reagents. The parallel-tube model offered lower variability in clearance predictions over the well-stirred model. For compounds that likely demonstrate passive permeability-limited clearance in vitro, rat LM predicted in vivo clearance more accurately than HEP. This comprehensive analysis demonstrated reliable IVIVE can be achieved using LMs and HEPs. Evaluation of clearance IVIVE in preclinical species helps to better understand clearance mechanisms, establish more reliable IVIVE in human, and enhance our confidence in human clearance and PK prediction, while considering species differences in drug metabolizing enzymes and transporters.

Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain-a Game Changing Parameter for CNS Drug Discovery and Development.

PURPOSE: More than 15 years have passed since the first description of the unbound brain-to-plasma partition coefficient (Kp,uu,brain) by Prof. Margareta Hammarlund-Udenaes, which was enabled by advancements in experimental methodologies including cerebral microdialysis. Since then, growing knowledge and data continue to support the notion that the unbound (free) concentration of a drug at the site of action, such as the brain, is the driving force for pharmacological responses. Towards this end, Kp,uu,brain is the key parameter to obtain unbound brain concentrations from unbound plasma concentrations. METHODS: To understand the importance and impact of the Kp,uu,brain concept in contemporary drug discovery and development, a survey has been conducted amongst major pharmaceutical companies based in Europe and the USA. Here, we present the results from this survey which consisted of 47 questions addressing: 1) Background information of the companies, 2) Implementation, 3) Application areas, 4) Methodology, 5) Impact and 6) Future perspectives. RESULTS AND CONCLUSIONS: From the responses, it is clear that the majority of the companies (93%) has established a common understanding across disciplines of the concept and utility of Kp,uu,brain as compared to other parameters related to brain exposure. Adoption of the Kp,uu,brain concept has been mainly driven by individual scientists advocating its application in the various companies rather than by a top-down approach. Remarkably, 79% of all responders describe the portfolio impact of Kp,uu,brain implementation in their companies as 'game-changing'. Although most companies (74%) consider the current toolbox for Kp,uu,brain assessment and its validation satisfactory for drug discovery and early development, areas of improvement and future research to better understand human brain pharmacokinetics/pharmacodynamics translation have been identified.

Effects of low temperature on blood-to-plasma ratio measurement.

The blood-to-plasma ratio (Rb ) is an important property of drug candidates. Rb is applied widely in drug discovery to convert plasma pharmacokinetic parameters to the respective parameters in blood and to develop in vitro-in vivo correlations. Some compounds such as prodrugs, soft drugs, and peptide mimetics are unstable in blood, making accurate in vitro Rb measurement challenging, but necessary. Low temperature often reduces the rate of enzymatic and chemical reactions and increases the stability of labile compounds in biomatrices. In this study, the effects of 4°C on Rb measurement were evaluated using a set of structurally diverse compounds with various binding and red blood cell (RBC) transport mechanisms. The results indicate that a 4°C Rb method provides comparable Rb values to the 37°C method for most compounds and can therefore be applied to measure the Rb of unstable compounds in drug discovery. In some rare cases, when compounds have a high affinity to specific RBC components (e.g., carbonic anhydrase), the 4°C method may underestimate Rb. In these specific cases, the use of appropriate inhibitors to stabilize unstable compounds is recommended.

Development of a risk-stratification scoring system for predicting lymphovascular invasion in breast cancer.

BACKGROUND: Lymphovascular invasion (LVI) is a vital risk factor for prognosis across cancers. We aimed to develop a scoring system for stratifying LVI risk in patients with breast cancer. METHODS: A total of 301 consecutive patients (mean age, 49.8 ± 11.0 years; range, 29-86 years) with breast cancer confirmed by pathological reports were retrospectively evaluated at the authors' institution between June 2015 and October 2018. All patients underwent contrast-enhanced Magnetic Resonance Imaging (MRI) examinations before surgery. MRI findings and histopathologic characteristics of tumors were collected for analysis. Breast LVI was confirmed by postoperative pathology. We used a stepwise logistic regression to select variables and two cut-points were determined to create a three-tier risk-stratification scoring system. The patients were classified as having low, moderate and high probability of LVI. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discrimination ability of the scoring system. RESULTS: Tumor margins, lobulation sign, diffusion-weighted imaging appearance, MRI-reported axillary lymph node metastasis, time to signal intensity curve pattern, and HER-2 were selected as predictors for LVI in the point-based scoring system. Patients were considered at low risk if the score was < 3.5, moderate risk if the score was 3.5 to 6.0, and high risk if the score was ≥6.0. LVI risk was segmented from 0 to 100.0% and was positively associated with an increase in risk scores. The AUC of the scoring system was 0.824 (95% confidence interval [CI]: 0.776--0.872). CONCLUSION: This study shows that a simple and reliable score-based risk-stratification system can be practically used in stratifying the risk of LVI in breast cancer.

In vitro and in vivo methods to assess pharmacokinetic drug- drug interactions in drug discovery and development.

Drug-drug interactions (DDIs) caused by the co-administration of multiple drugs are major safety concerns in the clinic. Several drugs have been withdrawn from the market due to perpetrator or victim DDIs. Strategies have been developed to assess DDI risks early in drug discovery to reduce DDI liabilities. High-to-medium throughput assays are available to identify undesirable scaffolds and to guide structural modifications to minimize DDIs. Definitive methods are used at later stages of drug discovery and development to provide a more accurate measurement of DDI parameters and to enable clinical translations. Physiologically based pharmacokinetic modeling and simulations are powerful tools to accurately predict DDIs and to assess risks in the clinic. Although significant advances have been made over the years, many challenges remain for clinical DDI translations. This includes DDIs involving non-cytochrome P450 enzymes, transporters, enzyme-transporter interplay, indirect effects from biologics, and pharmacodynamic based DDI. This review focuses on methods that are used to assess hepatic DDIs caused by enzyme inhibition and induction.

Applications of Protein Fragment Complementation Assays for Analyzing Biomolecular Interactions and Biochemical Networks in Living Cells.

Protein-protein interactions (PPIs) are indispensable for the dynamic assembly of multiprotein complexes that are central players of nearly all of the intracellular biological processes, such as signaling pathways, metabolic pathways, formation of intracellular organelles, establishment of cytoplasmic skeletons, etc. Numerous approaches have been invented to study PPIs both in vivo and in vitro, including the protein-fragment complementation assay (PCA), which is a widely applied technology to study PPIs and biomolecular interactions. PCA is a technology based on the expression of the bait and prey proteins in fusion with two complementary reporter protein fragments, respectively, that will reassemble when in close proximity. The reporter protein can be the enzymes or fluorescent proteins. Recovery of the enzymatic activity or fluorescent signal can be the indicator of PPI between the bait and prey proteins. Significant effort has been invested in developing many derivatives of PCA, along with various applications, in order to address specific questions. Therefore, a prompt review of these applications is important. In this review, we will categorize these applications according to the scenarios that the PCAs were applied and expect to provide a reference guideline for the future selection of PCA methods in solving a specific problem.

A Novel Unified Approach to Predict Human Hepatic Clearance for Both Enzyme- and Transporter-Mediated Mechanisms Using Suspended Human Hepatocytes.

The accurate prediction of human pharmacokinetics is critically important in modern drug discovery since it drives both pharmacological and toxicological effects. Although significant progress has been made in predicting drug disposition by hepatic drug-metabolizing enzymes, predicting transporter-mediated clearance is still highly uncertain. Furthermore, different approaches are often used to predict clearance with and without transporter involvement, hence the major clearance pathway for a compound must first be determined to know which approach to use. As a result of these challenges, a novel unified method has been developed using cryopreserved suspended human hepatocytes to predict human hepatic clearance for both enzyme- and transporter-mediated mechanisms. This method hypothesizes that, once in vitro metabolic stability is scaled by partition coefficients between hepatocytes and buffer with 4% bovine serum albumin, in vivo clearance can be better predicted. With this method, good in vitro-in vivo correlation of human hepatic clearance has been obtained for a set of 32 structurally diverse compounds, including such transporters as organic anion-transporting polypeptide substrates. The clearance predictions for most compounds are within 3-fold of observed values. This is the first time that multiple compounds result in good in vitro-in vivo extrapolation using an entirely "bottom-up" approach without any empirical scaling factor when transporter-mediated clearance is involved. Potential exceptions are compounds with significant biliary and/or extra-hepatic clearance. The method offers an alternative approach to more accurately predict human hepatic clearance when multiple complex mechanisms are involved.

Drug Concentration Asymmetry in Tissues and Plasma for Small Molecule-Related Therapeutic Modalities.

The well accepted "free drug hypothesis" for small-molecule drugs assumes that only the free (unbound) drug concentration at the therapeutic target can elicit a pharmacologic effect. Unbound (free) drug concentrations in plasma are readily measurable and are often used as surrogates for the drug concentrations at the site of pharmacologic action in pharmacokinetic-pharmacodynamic analysis and clinical dose projection in drug discovery. Furthermore, for permeable compounds at pharmacokinetic steady state, the free drug concentration in tissue is likely a close approximation of that in plasma; however, several factors can create and maintain disequilibrium between the free drug concentration in plasma and tissue, leading to free drug concentration asymmetry. These factors include drug uptake and extrusion mechanisms involving the uptake and efflux drug transporters, intracellular biotransformation of prodrugs, membrane receptor-mediated uptake of antibody-drug conjugates, pH gradients, unique distribution properties (covalent binders, nanoparticles), and local drug delivery (e.g., inhalation). The impact of these factors on the free drug concentrations in tissues can be represented by K p,uu, the ratio of free drug concentration between tissue and plasma at steady state. This review focuses on situations in which free drug concentrations in tissues may differ from those in plasma (e.g., K p,uu > or <1) and discusses the limitations of the surrogate approach of using plasma-free drug concentration to predict free drug concentrations in tissue. This is an important consideration for novel therapeutic modalities since systemic exposure as a driver of pharmacologic effects may provide limited value in guiding compound optimization, selection, and advancement. Ultimately, a deeper understanding of the relationship between free drug concentrations in plasma and tissues is needed.

Dasotraline as a selective cytochrome 2B6 inhibitor for reaction phenotyping.

Reaction phenotyping using human liver microsomes or hepatocytes with chemical inhibitors is one of the most commonly applied methods to assess the fraction metabolized (fm ) of drug candidates by enzymes. The fm information is critical to understanding the risk of victim drug-drug interactions in the clinic. Inhibitor selectivity is essential in order to generate reliable data and irreversible inhibitors are often preferred over reversible inhibitors to minimize the impact of inhibitor depletion. Although many selective cytochrome P450 (CYP) inhibitors are available, the identification of selective CYP2B6 inhibitors has been challenging due to cross inhibition to the other enzymes. In this study, dasotraline was evaluated as a selective inactivator of CYP2B6 under reaction phenotyping conditions with human hepatocytes. The results show that dasotraline is a very selective inactivator for CYP2B6 with minimal inhibition to other enzymes. A concentration of 0.1 µM dasotraline is recommended for reaction phenotyping with a hepatocyte cell density of 0.5 million cells/ml or 0.5 µM for 2 million cells/ml, when using a 15 minute preincubation, as well as the protocol of inactivator removal before the addition of substrates.