A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model of the histone deacetylase (HDAC) inhibitor vorinostat for pediatric and adult patients and its application for dose specification

Ketogenic Diet Alters the Epigenetic and Immune Landscape of Prostate Cancer to Overcome Resistance to Immune Checkpoint Blockade Therapy

Resistance to immune checkpoint blockade (ICB) therapy represents a formidable clinical challenge limiting the efficacy of immunotherapy. In particular, prostate cancer poses a challenge for ICB therapy due to its immunosuppressive features. A ketogenic diet (KD) has been reported to enhance response to ICB therapy in some other cancer models. However, adverse effects associated with continuous KD were also observed, demanding better mechanistic understanding and optimized regimens for using KD as an immunotherapy sensitizer. In this study, we established a series of ICB-resistant prostate cancer cell lines and developed a highly effective strategy of combining anti-PD1 and anti-CTLA4 antibodies with histone deacetylase inhibitor (HDACi) vorinostat, a cyclic KD (CKD), or dietary supplementation of the ketone body β-hydroxybutyrate (BHB), which is an endogenous HDACi. CKD and BHB supplementation each delayed prostate cancer tumor growth as monotherapy, and both BHB and adaptive immunity were required for the antitumor activity of CKD. Single-cell transcriptomic and proteomic profiling revealed that HDACi and ketogenesis enhanced ICB efficacy through both cancer cell-intrinsic mechanisms, including upregulation of MHC class I molecules, and -extrinsic mechanisms, such as CD8+ T-cell chemoattraction, M1/M2 macrophage rebalancing, monocyte differentiation toward antigen-presenting cells, and diminished neutrophil infiltration. Overall, these findings illuminate a potential clinical path of using HDACi and optimized KD regimens to enhance ICB therapy for prostate cancer.

SIGNIFICANCE

Optimized cyclic ketogenic diet and 1,3-butanediol supplementation regimens enhance the efficacy of immune checkpoint blockade in prostate cancer through epigenetic and immune modulations, providing dietary interventions to sensitize tumors to immunotherapy.

Gesicles packaging dCas9-VPR ribonucleoprotein complexes can combine with vorinostat and promote HIV proviral transcription

Despite the success of combination antiretroviral therapy (cART) in HIV treatment, a cure for HIV remains elusive. Scientists postulate that HIV latent reservoirs may be a vital target in curative strategies. Vorinostat is a latency-reversing agent that has demonstrated some effectiveness in reactivating latent HIV, but complementary therapies may be essential to enhance its efficacy. One such approach may utilize the CRISPR-Cas9 system, which has evolved to include transcriptional activators such as dCas9-VPR. In this study, we explored the effects of combining vorinostat coupled with gesicle-mediated delivery of dCas9-VPR in promoting the transcription of integrated HIV proviruses in HIV-NanoLuc CHME-5 microglia and J-Lat 10.6 lymphocytes. We confirmed that dCas9-VPR ribonucleoprotein complexes can be packaged into gesicles and application to cells successfully induced HIV transcription through interactions with the HIV LTR. Vorinostat also induced significant increases in proviral transcription but generated inhibition of cellular proliferation (microglia) or cell viability (lymphocytes) starting at 1,000 nM and higher concentrations. Experiments combining dCas9-VPR gesicles and vorinostat confirmed the enhanced transcriptional activation of the HIV provirus in microglia but not lymphocytes. Thus, a combination of dCas9-VPR gesicles with other latency-reversing agents may provide a complementary method to activate latent HIV in future studies utilizing patient-derived cells or small animal models.

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

INTRODUCTION

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

METHOD

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

RESULT

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

CONCLUSION

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

Overcome Prostate Cancer Resistance to Immune Checkpoint Therapy with Ketogenic Diet-Induced Epigenetic Reprogramming

Advanced prostate cancer (PCa) is overwhelmingly resistant to immune checkpoint blockade (ICB) therapy, representing a formidable clinical challenge. In this study, we developed a syngeneic murine PCa model with acquired ICB resistance. Using this model, synergistic efficacy was achieved by combining anti-PD1 and anti-CTLA4 antibodies with histone deacetylase inhibitor (HDACi) vorinostat, a cyclic ketogenic diet (CKD), or supplementation of ketone body β-hydroxybutyrate (BHB, endogenous HDACi) via 1,3-butanediol-admixed food. CKD and BHB supplementation delayed PCa tumors as monotherapy, and both BHB and adaptive immunity are required for the anti-tumor activity of CKD. Single-cell transcriptomic and proteomic profiling revealed that the HDACi and ketogenesis-enhanced ICB therapy involves cancer-cell-intrinsic (upregulated MHC class I molecules) and extrinsic mechanisms (CD8 + T cell chemoattraction, M1/M2 macrophage rebalancing, monocyte differentiation toward antigen presenting cells, and diminished neutrophils). Overall, these findings underscore the potential of using HDACi and optimized KD to enhance ICB therapy for PCa.

Predicting the correct dose in children: Role of computational Pediatric Physiological-based pharmacokinetics modeling tools

The pharmacokinetics (PKs) and safety of medications in particular groups can be predicted using the physiologically-based pharmacokinetic (PBPK) model. Using the PBPK model may enable safe pediatric clinical trials and speed up the process of new drug research and development, especially for children, a population in which it is relatively difficult to conduct clinical trials. This review summarizes the role of pediatric PBPK (P-PBPK) modeling software in dose prediction over the past 6 years and briefly introduces the process of general P-PBPK modeling. We summarized the theories and applications of this software and discussed the application trends and future perspectives in the area. The modeling software's extensive use will undoubtedly make it easier to predict dose prediction for young patients.

Leveraging modeling and simulation to optimize the therapeutic window for epigenetic modifier drugs

Dysregulated epigenetic processes can lead to altered gene expression and give rise to malignant transformation and tumorigenesis. Epigenetic drugs aim to revert the phenotype of cancer cells to normally functioning cells, and are developed and applied to treat both hematological and solid cancers. Despite this promising therapeutic avenue, the successful development of epigenetic modulators has been challenging. We argue that besides identifying the right responder patient population, the selection of an optimized dosing regimen is equally important. For the majority of epigenetic modulators, hematological adverse effects such as thrombocytopenia, anemia or neutropenia are frequently observed and may limit their therapeutic potential. Therefore, one of the key challenges is to identify a dosing regimen that maximizes drug efficacy and minimizes toxicity. This requires a good understanding of the quantitative relationship between the administered dose, the drug exposure and the magnitude and duration of drug response related to safety and efficacy. With case examples, we highlight how modeling and simulation has been successfully applied to address those questions. As an outlook, we suggest the combination of efficacy and safety prediction models that capture the quantitative, mechanistic relationships governing the balance between their safety and efficacy dynamics. A stepwise approach for its implementation is presented. Utilizing in silico explorations, the impact of dosing regimen on the therapeutic window can be explored. This will serve as a basis to select the most promising dosing regimen that maximizes efficacy while minimizing adverse effects and to increase the probability of success for the given epigenetic drug.

Ontogeny of Drug-Metabolizing Enzymes

Almost 50% of prescription drugs lack age-appropriate dosing guidelines and therefore are used "off-label." Only ~10% drugs prescribed to neonates and infants have been studied for safety or efficacy. Immaturity of drug metabolism in children is often associated with drug toxicity. This chapter summarizes data on the ontogeny of major human metabolizing enzymes involved in oxidation, reduction, hydrolysis, and conjugation of drugs. The ontogeny data of individual drug-metabolizing enzymes are important for accurate prediction of drug pharmacokinetics and toxicity in children. This information is critical for designing clinical studies to appropriately test pharmacological hypotheses and develop safer pediatric drugs, and to replace the long-standing practice of body weight- or surface area-normalized drug dosing. The application of ontogeny data in physiologically based pharmacokinetic model and regulatory submission are discussed.

A Physiologically Based Pharmacokinetic-Pharmacodynamic Model for Capecitabine in Colorectal Cancer Rats: Simulation of Antitumor Efficacy at Various Administration Schedules

BACKGROUND AND OBJECTIVES

Capecitabine is an oral prodrug of 5-fluorouracil and is widely used for colorectal cancer (CRC) treatment. However, knowledge of its antitumor efficacy after modification of the dosing schedule is insufficient. The aim of this study was to predict the antitumor efficacy of capecitabine using a physiologically based pharmacokinetic-pharmacodynamic (PBPK-PD) model based on metabolic enzyme activities.

METHODS

CRC model rats were administrated 180 mg/kg of capecitabine for 2 weeks. Blood samples were collected at 0, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 6, and 8 h following capecitabine administration. Plasma concentrations of capecitabine and its metabolites were measured on days 1, 7, and 14. Metabolic enzyme activities were determined in vitro using the liver and small intestine of the CRC model rats. A PBPK-PD model was developed based on metabolic enzyme activities. The antitumor efficacy of capecitabine after regimen modification was simulated using the PBPK-PD model.

RESULTS

Capecitabine antitumor efficacy was dose-dependent. A dose of > 500 μmol/kg was needed to inhibit tumor growth. After capecitabine regimen modification, a 1-week postponement of capecitabine administration was more efficacious than a reduction in the dosage to 80%.

CONCLUSIONS

The PBPK-PD model could simulate the antitumor efficacy at various capecitabine administration schedules. PBPK-PD models can contribute to the development of an appropriate CRC chemotherapy regimen with capecitabine.

Targeting the epigenome in in-stent restenosis: from mechanisms to therapy

Coronary artery disease (CAD) is one of the most common causes of death worldwide. The introduction of percutaneous revascularization has revolutionized the therapy of patients with CAD. Despite the advent of drug-eluting stents, restenosis remains the main challenge in treating patients with CAD. In-stent restenosis (ISR) indicates the reduction in lumen diameter after percutaneous coronary intervention, in which the vessel's lumen re-narrowing is attributed to the aberrant proliferation and migration of vascular smooth muscle cells (VSMCs) and dysregulation of endothelial cells (ECs). Increasing evidence has demonstrated that epigenetics is involved in the occurrence and progression of ISR. In this review, we provide the latest and comprehensive analysis of three separate but related epigenetic mechanisms regulating ISR, namely, DNA methylation, histone modification, and non-coding RNAs. Initially, we discuss the mechanism of restenosis. Furthermore, we discuss the biological mechanism underlying the diverse epigenetic modifications modulating gene expression and functions of VSMCs, as well as ECs in ISR. Finally, we discuss potential therapeutic targets of the small molecule inhibitors of cardiovascular epigenetic factors. A more detailed understanding of epigenetic regulation is essential for elucidating this complex biological process, which will assist in developing and improving ISR therapy.

High-throughput screening identifies histone deacetylase inhibitors that modulate GTF2I expression in 7q11.23 microduplication autism spectrum disorder patient-derived cortical neurons

Background

Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental condition affecting almost 1% of children, and represents a major unmet medical need with no effective drug treatment available. Duplication at 7q11.23 (7Dup), encompassing 26–28 genes, is one of the best characterized ASD-causing copy number variations and offers unique translational opportunities, because the hemideletion of the same interval causes Williams–Beuren syndrome (WBS), a condition defined by hypersociability and language strengths, thereby providing a unique reference to validate treatments for the ASD symptoms. In the above-indicated interval at 7q11.23, defined as WBS critical region, several genes, such as GTF2I, BAZ1B, CLIP2 and EIF4H, emerged as critical for their role in the pathogenesis of WBS and 7Dup both from mouse models and human studies.

Methods

We performed a high-throughput screening of 1478 compounds, including central nervous system agents, epigenetic modulators and experimental substances, on patient-derived cortical glutamatergic neurons differentiated from our cohort of induced pluripotent stem cell lines (iPSCs), monitoring the transcriptional modulation of WBS interval genes, with a special focus on GTF2I, in light of its overriding pathogenic role. The hits identified were validated by measuring gene expression by qRT-PCR and the results were confirmed by western blotting.

Results

We identified and selected three histone deacetylase inhibitors (HDACi) that decreased the abnormal expression level of GTF2I in 7Dup cortical glutamatergic neurons differentiated from four genetically different iPSC lines. We confirmed this effect also at the protein level.

Limitations

In this study, we did not address the molecular mechanisms whereby HDAC inhibitors act on GTF2I. The lead compounds identified will now need to be advanced to further testing in additional models, including patient-derived brain organoids and mouse models recapitulating the gene imbalances of the 7q11.23 microduplication, in order to validate their efficacy in rescuing phenotypes across multiple functional layers within a translational pipeline towards clinical use.

Conclusions

These results represent a unique opportunity for the development of a specific class of compounds for treating 7Dup and other forms of intellectual disability and autism.

Physiologically-Based Pharmacokinetic (PBPK) Modeling Providing Insights into Fentanyl Pharmacokinetics in Adults and Pediatric Patients

Fentanyl is widely used for analgesia, sedation, and anesthesia both in adult and pediatric populations. Yet, only few pharmacokinetic studies of fentanyl in pediatrics exist as conducting clinical trials in this population is especially challenging. Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic approach to explore drug pharmacokinetics and allows extrapolation from adult to pediatric populations based on age-related physiological differences. The aim of this study was to develop a PBPK model of fentanyl and norfentanyl for both adult and pediatric populations. The adult PBPK model was established in PK-Sim® using data from 16 clinical studies and was scaled to several pediatric subpopulations. ~93% of the predicted AUClast values in adults and ~88% in pediatrics were within 2-fold of the corresponding value observed. The adult PBPK model predicted a fraction of fentanyl dose metabolized to norfentanyl of ~33% and a fraction excreted in urine of ~7%. In addition, the pediatric PBPK model was used to simulate differences in peak plasma concentrations after bolus injections and short infusions. The novel PBPK models could be helpful to further investigate fentanyl pharmacokinetics in both adult and pediatric populations.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Phenoconversion of Cytochrome P450 Metabolism: A Systematic Review

Phenoconversion is the mismatch between the individual’s genotype-based prediction of drug metabolism and the true capacity to metabolize drugs due to nongenetic factors. While the concept of phenoconversion has been described in narrative reviews, no systematic review is available. A systematic review was conducted to investigate factors contributing to phenoconversion and the impact on cytochrome P450 metabolism. Twenty-seven studies met the inclusion criteria and were incorporated in this review, of which 14 demonstrate phenoconversion for a specific genotype group. Phenoconversion into a lower metabolizer phenotype was reported for concomitant use of CYP450-inhibiting drugs, increasing age, cancer, and inflammation. Phenoconversion into a higher metabolizer phenotype was reported for concomitant use of CYP450 inducers and smoking. Moreover, alcohol, pregnancy, and vitamin D exposure are factors where study data suggested phenoconversion. The studies reported genotype–phenotype discrepancies, but the impact of phenoconversion on the effectiveness and toxicity in the clinical setting remains unclear. In conclusion, phenoconversion is caused by both extrinsic factors and patient- and disease-related factors. The mechanism(s) behind and the extent to which CYP450 metabolism is affected remain unexplored. If studied more comprehensively, accounting for phenoconversion may help to improve our ability to predict the individual CYP450 metabolism and personalize drug treatment.

Toward precision medicine in pediatric population using cytochrome P450 phenotyping approaches and physiologically based pharmacokinetic modeling

The activity of drug-metabolizing enzymes (DME) shows high inter- and intra-individual variability. Genetic polymorphisms, exposure to drugs, and environmental toxins are known to significantly alter DME expression. In addition, the activity of these enzymes is highly age-dependent due to maturation processes that occur during development. Currently, there is a vast choice of phenotyping methods in adults using exogenous probes to characterize the activity of these enzymes. However, this can hardly be applied to children since it requires the intake of non-therapeutic xenobiotics. In addition, sampling may be challenging in the pediatric population for a variety of reasons: limited volume (e.g., blood), inappropriate sampling methods for age (e.g., urine), and metric requiring invasive or multiple blood samples. This review covers the main existing methods that can be used in the pediatric population to determine DME activity, with a particular focus on cytochrome P450 enzymes. Less invasive tools are described, including phenotyping using endogenous probes. Finally, the potential of pediatric model-informed precision dosing using physiologically based pharmacokinetic modeling is discussed.

Hybrid mesoporous nanorods with deeply grooved lateral faces toward cytosolic drug delivery

Hybrid mesoporous nanorods with six twisted sharp edges can induce effective penetration of intracellular barriers and cytosolic delivery of membrane-impermeable drugs through curvature effects.

Guiding dose adjustment of amlodipine after co-administration with ritonavir containing regimens using a physiologically-based pharmacokinetic/pharmacodynamic model

Amlodipine, a commonly prescribed anti-hypertensive drug, shows increased systemic exposure with cytochrome P450 (CYP) 3A inhibitors. Ritonavir (RTV) is a potent mechanism-based and reversible CYP3A inhibitor and moderate inducer that is used as a pharmacokinetic enhancer in several antiviral treatment regimens. Drug-drug interaction (DDI) between RTV and amlodipine is due to mixed inhibition and induction of CYP3A4, which is challenging to predict without a mechanistic model that accounts for the complexity of both mechanisms occurring simultaneously. A novel physiologically-based pharmacokinetic (PBPK) model was developed for amlodipine, and the model was verified using published clinical PK and DDI data. The verified amlodipine PBPK model was linked to a pharmacodynamics model that describes changes in systolic blood pressure (SBP) during and after co-administration with RTV. The magnitude and time course of RTV effects on amlodipine plasma exposures and SBP were evaluated, to provide guidance on dose adjustment of amlodipine during and after co-administration with RTV-containing regimens. Model simulations suggested that the increase in amlodipine's plasma exposure by RTV diminishes by approximately 80% within 5 days after the last dose of RTV. PBPK simulations suggested that resuming a full dose of amlodipine [5 mg once daily (QD)] immediately after RTV's last dose would decrease daily average SBP by a maximum of 3.3 mmHg, while continuing with the reduced dose (2.5 mg QD) for 5 days after the last dose of RTV would increase daily average SBP by a maximum of 5.8 mmHg. Based on these results, either approach of resuming amlodipine's full dose could be appropriate when combined with appropriate clinical monitoring.