Linking physiologically-based pharmacokinetic and genome-scale metabolic networks to understand estradiol biology

Physiologically-based pharmacokinetic modeling of prominent oral contraceptive agents and applications in drug-drug interactions

Considerable interest remains across the pharmaceutical industry and regulatory landscape in capabilities to model oral contraceptives (OCs), whether combined (COCs) with ethinyl estradiol (EE) or progestin-only pill. Acceptance of COC drug-drug interaction (DDI) assessment using physiologically-based pharmacokinetic (PBPK) is often limited to the estrogen component (EE), requiring further verification, with extrapolation from EE to progestins discouraged. There is a paucity of published progestin component PBPK models to support the regulatory DDI guidance for industry to evaluate a new chemical entity's (NCE's) DDI potential with COCs. Guidance recommends a clinical interaction study to be considered if an investigational drug is a weak or moderate inducer, or a moderate/strong inhibitor, of CYP3A4. Therefore, availability of validated OC PBPK models within one software platform, will be useful in predicting the DDI potential with NCEs earlier in the clinical development. Thus, this work was focused on developing and validating PBPK models for progestins, DNG, DRSP, LNG, and NET, within Simcyp, and assessing the DDI potential with known CYP3A4 inhibitors (e.g., ketoconazole) and inducers (e.g., rifampicin) with published clinical data. In addition, this work demonstrated confidence in the Simcyp EE model for regulatory and clinical applications by extensive verification in 70+ clinical PK and CYP3A4 interaction studies. The results provide greater capability to prospectively model clinical CYP3A4 DDI with COCs using Simcyp PBPK to interrogate the regulatory decision-tree to contextualize the potential interaction by known perpetrators and NCEs, enabling model-informed decision making, clinical study designs, and delivering potential alternative COC options for women of childbearing potential.

Management of perimenopausal and menopausal symptoms

Most women worldwide experience menopausal symptoms during the menopause transition or postmenopause. Vasomotor symptoms are most pronounced during the first four to seven years but can persist for more than a decade, and genitourinary symptoms tend to be progressive. Although the hallmark symptoms are hot flashes, night sweats, disrupted sleep, and genitourinary discomfort, other common symptoms and conditions are mood fluctuations, cognitive changes, low sexual desire, bone loss, increase in abdominal fat, and adverse changes in metabolic health. These symptoms and signs can occur in any combination or sequence, and the link to menopause may even be elusive. Estrogen based hormonal therapies are the most effective treatments for many of the symptoms and, in the absence of contraindications to treatment, have a generally favorable benefit:risk ratio for women below age 60 and within 10 years of the onset of menopause. Non-hormonal treatment options are also available. Although a symptom driven treatment approach with individualized decision making can improve health and quality of life for midlife women, menopausal symptoms remain substantially undertreated by healthcare providers.

Genome-scale metabolic modeling in antimicrobial pharmacology

The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades. This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant microbial pathogens. However, the detailed mechanisms of action, resistance, and toxicity of many antimicrobials remain uncertain, significantly hampering the development of novel antimicrobials. Genome-scale metabolic model (GSMM) has been increasingly employed to investigate microbial metabolism. In this review, we discuss the latest progress of GSMM in antimicrobial pharmacology, particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity, resistance, and toxicity. We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities (e.g., gene expression), identification of potential drug targets, and integration with machine learning and pharmacokinetic/pharmacodynamic modeling. Overall, GSMM has significant potential in elucidating the critical role of metabolic changes in antimicrobial pharmacology, providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.

Progesterone and estrogen levels are associated with live birth rates following artificial cycle frozen embryo transfers

PURPOSE

Does an association exist between serum progesterone and estradiol levels and live birth rates in artificial cycle frozen embryo transfer (AC-FET)?

METHODS

Retrospective cohort study was based on prospectively collected data at a university-affiliated fertility center. Included were all cycles using an artificial endometrial preparation with estradiol hemihydrate (Estrofem, 2 mg/8 h) and vaginal progesterone (Endometrin 100 mg/8 h), autologous oocytes, and cleavage stage embryo transfers. Serum progesterone and estradiol levels were measured 14 days after FET. A total of 921 cycles in 568 patients from to December 2010 to June 2019 were investigated. Live birth was the primary outcome measure.

RESULTS

Significant association was found between live birth and progesterone as well as estradiol levels (progesterone 14.65 vs 11.62 ng/ml, p = 0.001; estradiol 355.12 vs 287.67 pg/ml, p = 0.001). A significant difference in live birth rate was found below and above the median progesterone level (10.9 ng/ml, p = 0.007). Lower estradiol level was significantly associated with lower live birth rate (< 188.2 pg/ml 8.3%, > 263.1 pg/ml 16%, p = 0.02).

CONCLUSIONS

Serum progesterone and estradiol levels impact live birth rate in AC-FET.

Advances in flux balance analysis by integrating machine learning and mechanism-based models

The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.

Exploring Mechanistic Toxicity of Mixtures Using PBPK Modeling and Computational Systems Biology

Mixtures risk assessment needs an efficient integration of in vivo, in vitro, and in silico data with epidemiology and human studies data. This involves several approaches, some in current use and others under development. This work extends the Agency for Toxic Substances and Disease Registry physiologically based pharmacokinetic (PBPK) toolkit, available for risk assessors, to include a mixture PBPK model of benzene, toluene, ethylbenzene, and xylenes. The recoded model was evaluated and applied to exposure scenarios to evaluate the validity of dose additivity for mixtures. In the second part of this work, we studied toluene, ethylbenzene, and xylene (TEX)-gene-disease associations using Comparative Toxicogenomics Database, pathway analysis and published microarray data from human gene expression changes in blood samples after short- and long-term exposures. Collectively, this information was used to establish hypotheses on potential linkages between TEX exposures and human health. The results show that 236 genes expressed were common between the short- and long-term exposures. These genes could be central for the interconnecting biological pathways potentially stimulated by TEX exposure, likely related to respiratory and neuro diseases. Using publicly available data we propose a conceptual framework to study pathway perturbations leading to toxicity of chemical mixtures. This proposed methodology lends mechanistic insights of the toxicity of mixtures and when experimentally validated will allow data gaps filling for mixtures' toxicity assessment. This work proposes an approach using current knowledge, available multiple stream data and applying computational methods to advance mixtures risk assessment.

Insights from pharmacokinetic models of host-microbiome drug metabolism

Increasing evidence suggests a role of the gut microbiota in patients' response to medicinal drugs. In our recent study, we combined genomics of human gut commensals and gnotobiotic animal experiments to quantify microbiota and host contributions to drug metabolism. Informed by experimental data, we built a physiology-based pharmacokinetic model of drug metabolism that includes intestinal compartments with microbiome drug-metabolizing activity. This model successfully predicted serum levels of metabolites of three different drugs, quantified microbial contribution to systemic drug metabolite exposure, and simulated the effect of different parameters on host and microbiota drug metabolism. In this addendum, we expand these simulations to assess the effect of microbiota on the systemic drug and metabolite levels under conditions of altered host physiology, microbiota drug-metabolizing activity or physico-chemical properties of drugs. This work illustrates how and under which circumstances the gut microbiome may influence drug pharmacokinetics, and discusses broader implications of expanded pharmacokinetic models.

Estrogen Receptors and Ubiquitin Proteasome System: Mutual Regulation

This review provides information on the structure of estrogen receptors (ERs), their localization and functions in mammalian cells. Additionally, the structure of proteasomes and mechanisms of protein ubiquitination and cleavage are described. According to the modern concept, the ubiquitin proteasome system (UPS) is involved in the regulation of the activity of ERs in several ways. First, UPS performs the ubiquitination of ERs with a change in their functional activity. Second, UPS degrades ERs and their transcriptional regulators. Third, UPS affects the expression of ER genes. In addition, the opportunity of the regulation of proteasome functioning by ERs—in particular, the expression of immune proteasomes—is discussed. Understanding the complex mechanisms underlying the regulation of ERs and proteasomes has great prospects for the development of new therapeutic agents that can make a significant contribution to the treatment of diseases associated with the impaired function of these biomolecules.

SLCO1B1 genetic variation and hormone therapy in menopausal women

Objective:

Response to menopausal hormone therapy (MHT) shows individual variation. SLCO1B1 encodes the OATP1B1 transporter expressed in the liver that transports many endogenous substances, including estrone sulfate, from the blood into hepatocytes. This study evaluated the relationship between genetic variation in SLCO1B1 and response to MHT in women enrolled in the Kronos Early Estrogen Prevention Study (KEEPS) at Mayo Clinic, Rochester, MN.

Methods:

KEEPS participants were randomized to oral conjugated equine estrogen (n = 33, oCEE), transdermal 17β-estradiol (n = 33, tE₂), or placebo (n = 34) for 48 months. Menopausal symptoms (hot flashes, night sweats, insomnia, palpitations) were self-reported before treatment and at 48 months. Estrone (E₁), E₂, and sulfated conjugates (E₁S, E₂S) were measured using high-performance liquid chromatography-tandem mass spectrometry. SLCO1B1 rs4149056 (c.521T>C, p.Val174Ala) was genotyped using a TaqMan assay.

Results:

After adjusting for treatment, there was a significant association between the SLCO1B1 rs4149056 TT genotype (encoding normal function transporter) and lower E₁S, E₁S/E₁, and E₂S (P = 0.032, 0.010, and 0.008, respectively) compared with women who were heterozygous (TC) or homozygous (CC) for the reduced function allele. The interactions between genotype, treatment, and E₂S concentration were stronger in women assigned to tE₂ (P = 0.013) than the women taking oCEE (P = 0.056). Among women assigned to active treatment, women with the CT genotype showed a significantly greater decrease in night sweats (P = 0.041) than those with the TT genotype.

Conclusions:

Individual variation in sulfated estrogens is explained, in part, by genetic variation in SLCO1B1. Bioavailability of sulfated estrogens may contribute to relief of night sweats.

In silico models in drug development: where we are

The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.

Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation

Drug-induced perturbations of the endogenous metabolic network are a potential root cause of cellular toxicity. A mechanistic understanding of such unwanted side effects during drug therapy is therefore vital for patient safety. The comprehensive assessment of such drug-induced injuries requires the simultaneous consideration of both drug exposure at the whole-body and resulting biochemical responses at the cellular level. We here present a computational multi-scale workflow that combines whole-body physiologically based pharmacokinetic (PBPK) models and organ-specific genome-scale metabolic network (GSMN) models through shared reactions of the xenobiotic metabolism. The applicability of the proposed workflow is illustrated for isoniazid, a first-line antibacterial agent against Mycobacterium tuberculosis, which is known to cause idiosyncratic drug-induced liver injuries (DILI). We combined GSMN models of a human liver with N-acetyl transferase 2 (NAT2)-phenotype-specific PBPK models of isoniazid. The combined PBPK-GSMN models quantitatively describe isoniazid pharmacokinetics, as well as intracellular responses, and changes in the exometabolome in a human liver following isoniazid administration. Notably, intracellular and extracellular responses identified with the PBPK-GSMN models are in line with experimental and clinical findings. Moreover, the drug-induced metabolic perturbations are distributed and attenuated in the metabolic network in a phenotype-dependent manner. Our simulation results show that a simultaneous consideration of both drug pharmacokinetics at the whole-body and metabolism at the cellular level is mandatory to explain drug-induced injuries at the patient level. The proposed workflow extends our mechanistic understanding of the biochemistry underlying adverse events and may be used to prevent drug-induced injuries in the future.

The genotype of a patient determines the extent of drug-induced metabolic perturbations on the endogenous cellular network of the liver. A team around Lars Kuepfer at Germany’s RWTH Aachen University developed a computational workflow that links drug pharmacokinetics at the whole-body level with a cellular network of the liver. The authors used the competitive cofactor and energy demands in endogenous and drug metabolism to establish a multi-scale model for the antibiotic isoniazid. Their model quantitatively describes how isoniazid pharmacokinetics alter the intracellular liver biochemistry and the utilization of extracellular metabolites in different patient genotypes. The study outlines how a mechanistic understanding of genotype-dependent drug-induced metabolic perturbations may help to explain diverging incidence rates of toxic events in different patient subgroups. This could reduce the occurrence of toxic side effects during drug treatments in the future.