Developing a mechanistic understanding of the nonlinear pharmacokinetics of letermovir and prospective drug interaction with everolimus using physiological-based pharmacokinetic modeling.

Letermovir is approved for use in cytomegalovirus-seropositive hematopoietic stem cell transplant recipients and is investigated in other transplant settings. Nonlinear pharmacokinetics (PKs) were observed in clinical studies after intravenous and oral dosing across a wide dose range, including the efficacious doses of 240 and 480 mg. A physiologically-based PK (PBPK) model for letermovir was built to develop a plausible explanation for the nonlinear PKs observed in clinical studies.

Drug-Drug Interaction of Letermovir and Atorvastatin in Healthy Participants.

Letermovir (MK-8228/AIC246) is a cytomegalovirus (CMV) DNA terminase complex inhibitor for CMV prophylaxis in adult patients undergoing hematopoietic stem cell transplant. It is cytochrome P450 (CYP) 3A inhibitor and inhibits organic anion transporting polypeptide 1B1/3 and breast cancer resistance protein transporters. Atorvastatin (ATV), a commonly used treatment for hypercholesterolemia, is a substrate of organic anion transporting polypeptide 1B1, potentially breast cancer resistance protein, and CYP3A.

Two heads are better than one: current landscape of integrating QSP and machine learning : An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning.

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer 'omics' data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling.

From complex data to biological insight: 'DEKER' feature selection and network inference.

Network inference is a valuable approach for gaining mechanistic insight from high-dimensional biological data. Existing methods for network inference focus on ranking all possible relations (edges) among all measured quantities such as genes, proteins, metabolites (features) observed, which yields a dense network that is challenging to interpret. Identifying a sparse, interpretable network using these methods thus requires an error-prone thresholding step which compromises their performance.

Exposure-Response Analyses of Letermovir Following Oral and Intravenous Administration in Allogeneic Hematopoietic Cell Transplantation Recipients.

The cytomegalovirus (CMV) viral terminase inhibitor letermovir is approved for prophylaxis of CMV infection and disease in adult CMV-seropositive allogeneic hematopoietic stem cell transplantation recipients. In a phase III trial (NCT02137772), letermovir significantly reduced clinically significant CMV infection (CS-CMVi) rate vs. placebo through Week 24 (primary end point) and Week 14 (secondary end point) post transplantation.

Roadmap to 2030 for Drug Evaluation in Older Adults.

Changes that accompany older age can alter the pharmacokinetics (PK), pharmacodynamics (PD), and likelihood of adverse effects (AEs) of a drug. However, older adults, especially the oldest or those with multiple chronic health conditions, polypharmacy, or frailty, are often under-represented in clinical trials of new drugs. Deficits in the current conduct of clinical evaluation of drugs for older adults and potential steps to fill those knowledge gaps are presented in this communication.

Population pharmacokinetics of letermovir following oral and intravenous administration in healthy participants and allogeneic hematopoietic cell transplantation recipients.

Letermovir is indicated for prophylaxis of cytomegalovirus infection and disease in allogeneic hematopoietic stem cell transplant (HSCT) recipients. Two-stage population pharmacokinetic (PK) modeling of letermovir was conducted to support dose rationale and evaluate the impact of intrinsic/extrinsic factors. Data from healthy phase I study participants over a wide dose range were modeled to evaluate the effects of selected intrinsic factors, including pharmacogenomics; next, phase III HSCT-recipient data at steady-state following clinical doses were modeled.

A Model-Informed Drug Discovery and Development Strategy for the Novel Glucose-Responsive Insulin MK-2640 Enabled Rapid Decision Making.

A model-informed drug discovery and development strategy played a key role in the novel glucose-responsive insulin MK-2640's early clinical development strategy and supported a novel clinical trial paradigm to assess glucose responsiveness. The development and application of in silico modeling approaches by leveraging substantial published clinical insulin pharmacokinetic-pharmacodynamic (PKPD) data and emerging preclinical and clinical data enabled rapid quantitative decision making. Learnings can be applied to define PKPD properties of novel insulins that could become therapeutically meaningful for diabetic patients.

Effect of letermovir on levonorgestrel and ethinyl estradiol pharmacokinetics .

Objective: Letermovir is an inhibitor of the terminase complex of cytomegalovirus (CMV) used as prophylactic therapy in CMV-seropositive allogeneic hematopoietic stem cell transplant recipients. As the combination oral contraceptive (COC) levonorgestrel/ethinyl estradiol (LNG/EE) may be coadministered in this target transplant population, the effects of letermovir on the pharmacokinetics (PK) of LNG and EE were investigated.

Materials And Methods: This was a phase I, open-label, fixed-sequence, two-period study conducted in healthy women (18 - 65 years old) of non-childbearing potential (protocol number: MK-8228 035).

Pharmacogenetic Analysis of OATP1B1, UGT1A1, and BCRP Variants in Relation to the Pharmacokinetics of Letermovir in Previously Conducted Clinical Studies.

The cytomegalovirus (CMV) viral terminase inhibitor letermovir is indicated for prevention of CMV infection in CMV-seropositive allogeneic hematopoietic stem cell transplant recipients. In this analysis, functional variants in solute carrier organic anion transporter family member 1B1 (SLCO1B1), uridine diphosphate-glucuronosyltransferase 1A1 (UGT1A1), and breast cancer resistance protein (BCRP) were assessed for effects on letermovir pharmacokinetics (PK) using pooled genetic information from 296 participants in 12 phase 1 studies. Letermovir area under the plasma concentration-time curve (AUC) was increased in carriers of the SLCO1B1 variant rs4149056 C allele relative to noncarriers with a geometric mean ratio (GMR) of 1.

Pharmacokinetic Drug-Drug Interactions Between Letermovir and the Immunosuppressants Cyclosporine, Tacrolimus, Sirolimus, and Mycophenolate Mofetil.

Letermovir (AIC246, MK-8228) is a human cytomegalovirus terminase inhibitor indicated for the prophylaxis of cytomegalovirus infection and disease in allogeneic hematopoietic stem cell transplant recipients that is also being investigated for use in other transplant settings. Many transplant patients receive immunosuppressant drugs, of which several have narrow therapeutic ranges. There is a potential for the coadministration of letermovir with these agents, and any potential effect on their pharmacokinetics (PK) must be understood.

PBPK Modeling Strategy for Predicting Complex Drug Interactions of Letermovir as a Perpetrator in Support of Product Labeling.

Letermovir is a human cytomegalovirus (CMV) terminase inhibitor for the prophylaxis of CMV infection in allogeneic hematopoietic stem-cell transplant (HSCT) recipients. In vitro, letermovir is a time-dependent inhibitor and an inducer of cytochrome P450 (CYP)3A, and an inhibitor of CYP2C8 and organic anion transporting polypeptide (OATP)1B. A stepwise approach was taken to qualify the interaction model of an existing letermovir physiologically based pharmacokinetic model to predict letermovir interactions with CYP3A and OATP1B.

Pharmacokinetics and Tolerability of Letermovir Coadministered With Azole Antifungals (Posaconazole or Voriconazole) in Healthy Subjects.

Letermovir is a human cytomegalovirus terminase inhibitor for cytomegalovirus infection prophylaxis in hematopoietic stem cell transplant recipients. Posaconazole (POS), a substrate of glucuronosyltransferase and P-glycoprotein, and voriconazole (VRC), a substrate of CYP2C9/19, are commonly administered to transplant recipients. Because coadministration of these azoles with letermovir is expected, the effect of letermovir on exposure to these antifungals was investigated.

Surgical margins and risk of locoregional recurrence in invasive breast cancer: an analysis of 10-year data from the Breast Cancer Treatment Quality Assurance Project.

Aim: There is debate as to what constitutes an adequate excision margin to reduce the risk of locoregional recurrence (LRR) after breast cancer surgery. We have investigated the relationship between surgical margin distance and LRR in women with invasive breast cancer (IBC).

Methods: Tumour free margin distances were extracted from histopathology reports for women with IBC, treated by either breast conserving surgery or mastectomy, enrolled in the Breast Cancer Treatment Group Quality Assurance Project from July 1997 to June 2007.

Colonoscopic preoperative localization using submucosal injection of radiolabelled colloid.

Malignant colonic polyps can be removed endoscopically but surgical resection is sometimes required. However, the polypectomy site can be difficult to locate. Current methods use various tattooing agents, with varying degrees of success.

Prediction of graft-versus-host disease in humans by donor gene-expression profiling.

Background: Graft-versus-host disease (GVHD) results from recognition of host antigens by donor T cells following allogeneic hematopoietic cell transplantation (AHCT). Notably, histoincompatibility between donor and recipient is necessary but not sufficient to elicit GVHD. Therefore, we tested the hypothesis that some donors may be "stronger alloresponders" than others, and consequently more likely to elicit GVHD.

The application of systems biology to drug discovery.

Recent advances in the 'omics' technologies, scientific computing and mathematical modeling of biological processes have started to fundamentally impact the way we approach drug discovery. Recent years have witnessed the development of genome-scale functional screens, large collections of reagents, protein microarrays, databases and algorithms for data and text mining. Taken together, they enable the unprecedented descriptions of complex biological systems, which are testable by mathematical modeling and simulation.

Response to continuous and pulsatile PTH dosing: a mathematical model for parathyroid hormone receptor kinetics.

In this paper, we propose a mathematical model for parathyroid hormone receptor (PTH1R) kinetics, focusing on the receptor's response to PTH dosing to discern bone formation responses from bone resorption. The PTH1R is a major target for new osteoporosis treatments, as pulsatile PTH dosing has been shown to induce net bone formation in both animals and humans, and PTH(1-34) was recently FDA approved for the treatment of post-menopausal osteoporosis. PTH has also been shown to cause net bone loss when given continuously, so that the net action of PTH on bone is dependent on the dosing pattern.

Modeling the interactions between osteoblast and osteoclast activities in bone remodeling.

We propose a mathematical model explaining the interactions between osteoblasts and osteoclasts, two cell types specialized in the maintenance of the bone integrity. Bone is a dynamic, living tissue whose structure and shape continuously evolves during life. It has the ability to change architecture by removal of old bone and replacement with newly formed bone in a localized process called remodeling.

Using in silico biology to facilitate drug development.

G protein-coupled receptor (GPCR) mediation of cardiac excitability is often overlooked in predicting the likelihood that a compound will alter repolarization. While the areas of GPCR signal transduction and electrophysiology are rich in data, experiments combining the two are difficult. In silico modelling facilitates the integration of all relevant data in both areas to explore the hypothesis that critical associations may exist between the different GPCR signalling mechanisms and cardiac excitability and repolarization.