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. In vitro studies suggested that letermovir elimination and distribution are mediated by saturable uridine glucuronosyltransferases (UGT)-metabolism and by saturable hepatic uptake via organic anion-transporting polypeptides (OATP) 1B. A sensitivity analysis of parameters describing the metabolism and distribution mechanisms indicated that the greater than dose-proportional increase in letermovir exposure is best described by a saturable OATP1B-mediated transport. This PBPK model was further used to evaluate the drug interaction potential between letermovir and everolimus, an immunosuppressant that may be co-administered with letermovir depending on regions. Because letermovir inhibits cytochrome P450 (CYP) 3A and everolimus is a known CYP3A substrate, an interaction when concomitantly administered is anticipated. The drug-drug interaction simulation confirmed that letermovir will likely increase everolimus are under the curve by 2.5-fold, consistent with the moderate increase in exposure observed with midazolam in the clinic. The output highlights the importance of drug monitoring, which is common clinical practice for everolimus to maintain safe and efficacious drug concentrations in the targeted patient population when concomitantly administered with letermovir.

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. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

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. As letermovir may be coadministered with ATV, the effect of multiple-dose letermovir 480 mg once daily on the pharmacokinetics of single-dose ATV 20 mg and its metabolites (ortho-hydroxyatorvastatin [o-OH-ATV] and para-hydroxyatorvastatin [p-OH-ATV]) was evaluated in an open-label trial in healthy female adults (N = 14). ATV area under the plasma concentration-time curve from time 0 to infinity and maximum plasma concentration (Cmax ) increased ≈3-fold with letermovir coadministration. The time to ATV Cmax also increased, while apparent clearance decreased. The exposures of o-OH-ATV and p-OH-ATV were comparable in the presence versus absence of letermovir; however, o-OH-ATV Cmax decreased by 60% with coadministration, while p-OH-ATV Cmax was similar. Due to the increase in ATV exposure with letermovir coadministration, statin-associated adverse events such as myopathy should be closely monitored following coadministration. The dose of ATV should not exceed 20 mg daily when coadministered with letermovir.

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. Here, exposure-response relationships were investigated using efficacy and selected safety end points from the phase III trial to inform the proposed clinical dose. Post hoc exposure estimates were derived from a population pharmacokinetic model. No significant exposure dependencies were found for CS-CMVi through Week 24 or Week 14 among letermovir-treated participants. Evaluated covariates had no impact on exposure-efficacy relationships and letermovir plasma exposure did not affect time of CS-CMVi onset. There was no dependence between adverse event incidence and letermovir exposure. These results support current dosing recommendations in several countries and regions, including the United States and European Union.

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. In this article we propose a new method, DEKER-NET, that addresses this limitation by directly identifying a sparse, interpretable network without thresholding, improving real-world performance. DEKER-NET uses a novel machine learning method for feature selection in an iterative framework for network inference. DEKER-NET is extremely flexible, handling linear and nonlinear relations while making no assumptions about the underlying distribution of data, and is suitable for categorical or continuous variables. We test our method on the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge data, demonstrating that it can directly identify sparse, interpretable networks without thresholding while maintaining performance comparable to the hypothetical best-case thresholded network of other methods.

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. The model in HSCT recipients adequately described letermovir PK following both oral or i.v. administration, and was consistent with the healthy participant model at steady-state clinical doses. Intrinsic factor effects were not clinically meaningful. These staged analyses indicate that letermovir PK in HSCT recipients and healthy participants differ only with respect to bioavailability and absorption rate. The HSCT recipient model was suitable for predicting exposure for exposure-response analysis supporting final dose selection.

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
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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). On day 1 of period 1, participants received a single dose of COC (LNG 0.15 mg/EE 0.03 mg). Following a 7-day washout, oral letermovir 480 mg was administered once-daily on days 1 - 12 of period 2, with a single dose of COC coadministered on day 8. Blood samples were collected to determine LNG and EE PK, and safety was assessed. RESULTS: The AUC0-∞ geometric mean ratios (90% confidence intervals) for COC + letermovir/COC alone were 1.36 (1.30, 1.43) for LNG and 1.42 (1.32, 1.52) for EE, indicating that letermovir coadministration increased COC exposure. Coadministration had no clinically-meaningful effect on Cmax, tmax, or apparent terminal T1/2 for either LNG or EE. All treatments were generally well tolerated. CONCLUSION: Letermovir coadministration with COC resulted in an increase in LNG and EE exposure in healthy adult women; however, levels were within the established safety margins. There was no decrease in LNG or EE exposure with no apparent risk of contraceptive failure on coadministration of letermovir and COC.
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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 1.18 (95% confidence interval [CI], 1.06-1.30) for carriers of 1 copy and 1.42 (1.10-1.84) for carriers of 2 copies of the risk allele C compared with noncarriers. The SLCO1B1 variant rs4149032 T allele was associated with a decrease in letermovir AUC with GMR (95%CI) of 0.93 (0.85-1.02) and 0.82 (0.73-0.92) for carriers of 1 and 2 copies of the risk allele T, respectively, compared with noncarriers. The UGT1A1*6 variant rs4148323 A allele was present predominantly in Asian participants and was associated with an increase in letermovir AUC compared with noncarriers (GMR, 1.36; 95%CI, 1. 1.07-1.74). SLCO1B1 variant rs2306283, UGT1A1*28 TA promoter repeat, and BCRP variant rs2231142 had no effect on letermovir PK. Together, these data suggest that variants of enzymes and transporters that are involved in the disposition of letermovir in vitro may account for some variability in letermovir PK, but do not affect exposure to a clinically relevant extent.

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. Five phase 1 trials were conducted in 73 healthy female participants to evaluate the effect of letermovir on the PK of cyclosporine, tacrolimus, sirolimus, and mycophenolic acid (active metabolite of mycophenolate mofetil [MMF]), as well as the effect of cyclosporine and MMF on letermovir PK. Safety and tolerability were also assessed. Coadministration of letermovir with cyclosporine, tacrolimus, and sirolimus resulted in 1.7-, 2.4-, and 3.4-fold increases in area under the plasma concentration-time curve and 1.1-, 1.6-, and 2.8-fold increases in maximum plasma concentration, respectively, of the immunosuppressants. Coadministration of letermovir and MMF had no meaningful effect on the PK of mycophenolic acid. Coadministration with cyclosporine increased letermovir area under the plasma concentration-time curve by 2.1-fold and maximum plasma concentration by 1.5-fold, while coadministration with MMF did not meaningfully affect the PK of letermovir. Given the increased exposures of cyclosporine, tacrolimus, and sirolimus, frequent monitoring of concentrations should be performed during administration and at discontinuation of letermovir, with dose adjustment as needed. These data support the reduction in clinical dosage of letermovir (to 240 mg) upon coadministration with cyclosporine.

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. The model was then used to prospectively predict the interaction between letermovir and CYP2C8 substrates such as repaglinide, a substrate of CYP2C8, CYP3A, and OATP1B. The results showed that letermovir modestly increased the exposure of CYP2C8 substrates. These results were used to inform the US prescribing information in the absence of clinical drug-drug interaction studies. In addition, midazolam interactions with letermovir at therapeutic doses were also simulated to confirm that letermovir is a moderate CYP3A inhibitor.

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. Two trials were conducted in healthy female subjects 18 to 55 years of age. In trial 1, single-dose POS 300 mg was administered alone, followed by a 7-day washout; then letermovir 480 mg once daily was given for 14 days with POS 300 mg coadministered on day 14. In trial 2, on day 1 VRC 400 mg was given every 12 hours; on days 2 and 3, VRC 200 mg was given every 12 hours, and on day 4 VRC 200 mg. On days 5 to 8, letermovir 480 mg was given once daily. Days 9 to 12 repeated days 1 to 4 coadministered with letermovir 480 mg once daily. In both trials, blood samples were collected for the assessment of the pharmacokinetic profiles of the antifungals, and safety was assessed. The geometric mean ratios (90%CIs) for POS+letermovir/POS area under the curve and peak concentration were 0.98 (0.83, 1.17) and 1.11 (0.95, 1.29), respectively. Voriconazole+letermovir/VRC area under the curve and peak concentration geometric mean ratios were 0.56 (0.51, 0.62) and 0.61 (0.53, 0.71), respectively. All treatments were generally well tolerated. Letermovir did not affect POS pharmacokinetics to a clinically meaningful extent but decreased VRC exposure. These results suggest that letermovir may be a perpetrator of CYP2C9/19-mediated drug-drug interactions.

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. METHODS AND FINDINGS: To this end, we measured the gene-expression profiles of CD4(+) and CD8(+) T cells from 50 AHCT donors with microarrays. We report that pre-AHCT gene-expression profiling segregates donors whose recipient suffered from GVHD or not. Using quantitative PCR, established statistical tests, and analysis of multiple independent training-test datasets, we found that for chronic GVHD the "dangerous donor" trait (occurrence of GVHD in the recipient) is under polygenic control and is shaped by the activity of genes that regulate transforming growth factor-beta signaling and cell proliferation. CONCLUSIONS: These findings strongly suggest that the donor gene-expression profile has a dominant influence on the occurrence of GVHD in the recipient. The ability to discriminate strong and weak alloresponders using gene-expression profiling could pave the way to personalized transplantation medicine.

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. While the methods and tools are advancing, it is their iterative and combinatorial application that defines the systems biology approach.

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. We have developed a simplified two-state receptor kinetics model for the PTH1R, based on the concepts of Segel et al., to distinguish the activity of active and inactive receptor and receptor-ligand complexes. The goal is to develop a plausible model of the minimal essential biological relationships necessary for understanding the responses to PTH dosing. A two-state model is able to effectively discriminate between continuous and pulsatile PTH dosing using the active species as surrogates for the downstream anabolic response. For continuous PTH dosing, the model predicts a desensitized system dominated by the inactive receptor and complex, consistent with downstream net bone loss that has been demonstrated experimentally. Using pulsatile PTH dosing, the model system predicts a highly sensitized state dominated by the active receptor and complex, corresponding to net bone formation. These results are consistent with the hypothesis that the kinetics of the receptor plays a critical role in the downstream effects of PTH dosing. Moreover, these results indicate that within a range of biologically relevant PTH doses, the two-state model is able to capture the differential behavior of the system for both continuous and pulsatile PTH dosing. The development of such a model provides a rational basis for developing more biologically extensive models that may support the design of optimal dosing strategies for PTH-based anti-osteoporosis treatments. Moreover, this model provides a unique starting point from which to design experiments investigating PTH receptor biology.

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. The model described here is based on the idea that the relative proportions of immature and mature osteoblasts control the degree of osteoclastic activity. In addition, osteoclasts control osteoblasts differentially depending on their stage of differentiation. Despite the tremendous complexity of the bone regulatory system and its fragmentary understanding, we obtain surprisingly good correlations between the model simulations and the experimental observations extracted from the literature. The model results corroborate all behaviors of the bone remodeling system that we have simulated, including the tight coupling between osteoblasts and osteoclasts, the catabolic effect induced by continuous administration of PTH, the catabolic action of RANKL, as well as its reversal by soluble antagonist OPG. The model is also able to simulate metabolic bone diseases such as estrogen deficiency, vitamin D deficiency, senescence and glucocorticoid excess. Conversely, possible routes for therapeutic interventions are tested and evaluated. Our model confirms that anti-resorptive therapies are unable to partially restore bone loss, whereas bone formation therapies yield better results. The model enables us to determine and evaluate potential therapies based on their efficacy. In particular, the model predicts that combinations of anti-resorptive and anabolic therapies provide significant benefits compared with monotherapy, especially for certain type of skeletal disease. Finally, the model clearly indicates that increasing the size of the pool of preosteoblasts is an essential ingredient for the therapeutic manipulation of bone formation. This model was conceived as the first step in a bone turnover modeling platform. These initial modeling results are extremely encouraging and lead us to proceed with additional explorations into bone turnover and skeletal 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. An example of this linkage is suggested by the observation that a mutation of the gene encoding HERG, the pore-forming subunit of the rapidly activating delayed rectifier K+ current (I(Kr)), leads to a form of long QT syndrome in which affected individuals are vulnerable to stress-induced arrhythmia following beta-adrenergic stimulation. Using Physiome's In Silico Cell, we constructed a model integrating the signalling mechanisms of second messengers cAMP and protein kinase A with I(Kr) in a cardiac myocyte. We analysed the model to identify the second messengers that most strongly influence I(Kr) behaviour. Our conclusions indicate that the dynamics of regulation are multifactorial, and that Physiome's approach to in silico modelling helps elucidate the subtle control mechanisms at play.