Understanding the pharmacokinetics and glucodynamics of once weekly basal insulins to inform dosing principles: an introduction to clinicians.

OBJECTIVE: A new generation of basal insulin analogs enabling once-weekly administration is currently under development. Weekly basal insulins have the potential to overcome limitations exhibited by current daily basal insulins. The pharmacokinetic and glucodynamic characteristics differ significantly between weekly and daily basal insulins and will require paradigm shifts in how basal insulins are dosed. METHODS: An overview of pharmacokinetic and glucodynamic principles of basal insulins is presented. Specifically, the pharmacokinetic and glucodynamic properties of daily basal insulins and how these differ for the new weekly basal insulins are discussed. Finally, models and simulations are used to describe the impact of weekly insulin properties on dosing. RESULTS: Two approaches have been used to extend the half-lives of these insulins, creating fusion proteins with reduced clearance and reduced receptor-mediated degradation of the insulin. The resulting prolonged exposure-response profiles affect dosing and the impact of dosing errors. Specifically, the impact of loading doses, missed doses, and double doses, and the effect on glycemic variability of a once weekly basal insulin option are demonstrated using pharmacokinetic/glucodynamic models and simulations. CONCLUSIONS: The transition from daily to weekly basal insulin dosing requires an understanding of the implications of the prolonged exposure-response profiles to effectively and confidently incorporate these weekly basal insulins into clinical practice. By reviewing the application of pharmacokinetic and glucodynamic principles to daily basal insulin analogs, the differences with weekly basal insulins, and the impact of these properties on dosing, this review intends to explain the principles behind weekly basal insulin dosing.

Pharmacokinetic and pharmacodynamic properties of the novel basal insulin Fc (insulin efsitora alfa), an insulin fusion protein in development for once-weekly dosing for the treatment of patients with diabetes.

AIM: To assess the safety, tolerability, pharmacokinetics (PK) and pharmacodynamics (PD) of basal insulin Fc (BIF; LY3209590), a fusion protein combining a novel single-chain insulin variant together with human IgG2 Fc domain, following single and multiple once-weekly BIF administration. MATERIALS AND METHODS: The single ascending dose, 15-day study assessed four BIF doses (5-35 mg) in healthy participants and people with type 2 diabetes (T2D). In the 6-week multiple ascending dose study, people with T2D, previously treated with basal insulin, received insulin glargine daily or a one-time loading dose of BIF followed by 5 weeks of once-weekly dosing (1-10 mg). Safety, tolerability and PK and glucose PD were examined. RESULTS: Mean ages of people with T2D (N = 57) and healthy participants (N = 16) in the single-dose study were 58.4 and 35.8 years, respectively; mean body mass index values were 29.5 and 26.1 kg/m2 . BIF had a PK half-life of approximately 17 days, which led to a sustained, dose-dependent decrease in fasting blood glucose for 5 days or longer. No severe hypoglycaemia was observed. The 6-week ascending dose study included 33 people with T2D aged 40-69 years. BIF showed a low peak-to-trough ratio of 1.14 after the last dose at week 6 (steady state). Over 6 weeks, BIF seven-point glucose profiles remained constant and were similar to insulin glargine. Rates and duration of BIF hypoglycaemic events were similar to insulin glargine. CONCLUSIONS: BIF was well tolerated and the PK/PD profile enabled once-weekly dosing with minimal variation in exposure in a treatment interval of 1 week. The findings suggest BIF is suitable for further development as a weekly basal insulin in people with diabetes.

Optimization of drug-drug interaction study design: comparison of minimal physiologically based pharmacokinetic models on prediction of CYP3A inhibition by ketoconazole.

Ketoconazole is a potent CYP3A inhibitor used to assess the contribution of CYP3A to drug clearance and quantify the increase in drug exposure due to a strong inhibitor. Physiologically based pharmacokinetic (PBPK) models have been used to evaluate treatment regimens resulting in maximal CYP3A inhibition by ketoconazole but have reached different conclusions. We compare two PBPK models of the ketoconazole-midazolam interaction, model 1 (Chien et al., 2006) and model 2 implemented in Simcyp (version 11), to predict 16 published treatment regimens. With use of model 2, 41% of the study point estimates of area under the curve (AUC) ratio and 71% of the 90% confidence intervals were predicted within 1.5-fold of the observed, but these increased to 82 and 100%, respectively, with model 1. For midazolam, model 2 predicted a maximal midazolam AUC ratio of 8 and a hepatic fraction metabolized by CYP3A (f(m)) of 0.97, whereas model 1 predicted 17 and 0.90, respectively, which are more consistent with observed data. On the basis of model 1, ketoconazole (400 mg QD) for at least 3 days and substrate administration within 2 hours is required for maximal CYP3A inhibition. Ketoconazole treatment regimens that use 200 mg BID underestimate the systemic fraction metabolized by CYP3A (0.86 versus 0.90) for midazolam. The systematic underprediction also applies to CYP3A substrates with high bioavailability and long half-lives. The superior predictive performance of model 1 reflects the need for accumulation of ketoconazole at enzyme site and protracted inhibition. Model 2 is not recommended for inferring optimal study design and estimation of fraction metabolized by CYP3A.

Safety and efficacy of once-weekly basal insulin Fc in people with type 2 diabetes previously treated with basal insulin: a multicentre, open-label, randomised, phase 2 study.

BACKGROUND: The burden of daily basal insulins often causes hesitancy and delays in the initiation of insulin therapy. Basal insulin Fc (BIF, insulin efsitora alfa), designed for once-weekly administration, is a fusion protein combining a novel single-chain insulin variant with a human immunoglobulin G (IgG) Fc domain. In this study, we explored the safety and efficacy of BIF in people with type 2 diabetes who had been previously treated with basal insulin. METHODS: For this phase 2, 44-site (clinical research centres and hospitals), randomised, open-label, comparator-controlled, 32-week study in the USA, Puerto Rico, and Mexico, we enrolled participants with type 2 diabetes. Eligible participants had to be adults (aged ≥18 years) and to have been treated with basal insulin and up to three oral antidiabetic medicines. Participants were randomly assigned (1:1:1) to subcutaneous administration of BIF (BIF treatment group 1 [BIF-A1] or 2 [BIF-A2]) or insulin degludec. Randomisation was stratified by country, baseline HbA1c values (<8·5% or ≥8·5%; <69·4 or ≥69·4 mmol/mol), use of sulfonylureas (yes or no), and baseline BMI (<30 or ≥30 kg/m2). The randomisation scheme was performed using an interactive web-response system, which ensured balance between treatment groups. Different fasting glucose targets for the BIF-A1 (≤7·8 mmol/L or ≤140 mg/dL; titrated every 2 weeks), BIF-A2 (≤6·7 mmol/L or ≤120 mg/dL; titrated every 4 weeks), and degludec (≤5·6 mmol/L or ≤100 mg/dL) groups were selected. Patients randomly assigned to BIF received a one-time loading dose ranging from 1·5-3 times their calculated weekly dose. The first weekly dose was administered 1 week after the loading dose. We used interstitial fasting glucose measurements from the Dexcom G6 continuous glucose monitoring system to titrate the basal insulin. The primary measure of glycaemic control was change in HbA1c from baseline to week 32 for BIF. BIF was also compared with degludec (with a non-inferiority margin of 0·40%). The efficacy analysis set consisted of data from all randomised study participants who received at least one dose of the study medication and participants were analysed according to the treatment they were assigned. The safety population was the same as the efficacy analysis set. The completed trial is registered at ClinicalTrials.gov (NCT03736785). FINDINGS: Between Nov 15, 2018 and Feb 18, 2020, 399 participants were enrolled and randomised to BIF-A1 (n=135), BIF-A2 (n=132), or degludec (n=132); 202 (51%) were female and 197 (49%) were male. 379 were analysed for the primary outcome (BIF-A1: n=130; BIF-A2: n=125; degludec: n=124). Mean HbA1c change from baseline to week 32, the primary outcome, was -0·6% (SE 0·1%) for BIF-A1 and BIF-A2. Degludec achieved a change from baseline of -0·7% (0·1%). The pooled BIF analysis achieved non-inferiority versus degludec for the treatment difference in HbA1c (0·1% [90% CI -0·1 to 0·3]). The hypoglycaemia (≤3·9 mmol/L or ≤70 mg/dL) event rates (hypoglycaemia events per patient per year) in the BIF groups were 25% lower than those in the degludec group (treatment ratio BIF-A1 vs degludec was 0·75 [0·61-0·93]; and BIF-A2 vs degludec was 0·74 [0·58-0·94]). BIF was well tolerated; treatment-emergent adverse events were similar across groups. INTERPRETATION: Weekly BIF achieved a similar efficacy compared with degludec despite higher fasting glucose targets in the BIF groups. Higher fasting glucose targets and lower glucose variability might have contributed to lower hypoglycaemia rates for BIF compared with degludec. These findings support continued development of BIF as a once-weekly insulin treatment for people with diabetes. FUNDING: Eli Lilly and Company.

Once-Weekly Basal Insulin Fc Demonstrated Similar Glycemic Control to Once-Daily Insulin Degludec in Insulin-Naive Patients With Type 2 Diabetes: A Phase 2 Randomized Control Trial.

OBJECTIVE: Basal insulin Fc (BIF) (insulin efsitora alfa; LY3209590), a fusion protein combining a novel single-chain insulin variant with a human IgG Fc domain, is designed for once-weekly basal insulin administration. This phase 2 study assessed the safety and efficacy of BIF versus degludec in insulin-naive patients with type 2 diabetes (T2D) previously treated with oral antihyperglycemic medications. RESEARCH DESIGN AND METHODS: During this randomized, parallel, open-label study, 278 insulin-naive patients with T2D were randomly assigned (1:1) to receive BIF once weekly or degludec once daily over the 26-week treatment period. Both groups were titrated to fasting glucose of 80-100 mg/dL (4.4 to <5.6 mmol/L). The primary end point was HbA1c change from baseline to week 26 (noninferiority margin 0.4%). Secondary end points included fasting blood glucose (FBG), six-point glucose profiles, and rate of hypoglycemia. RESULTS: After 26 weeks of treatment, BIF demonstrated a noninferior HbA1c change from baseline versus degludec, with a treatment difference of 0.06% (90% CI -0.11, 0.24; P = 0.56). Both BIF and degludec treatment led to significant reductions in FBG from baseline. At week 26, the between-treatment difference for BIF versus degludec was 4.7 mg/dL (90% CI 0.1, 9.3; P = 0.09). The rate of level 2 hypoglycemia was low and not significantly different between treatment groups (BIF 0.22 events/patient/year, degludec 0.15 events/patient/year; P = 0.64); there was no severe hypoglycemia. The occurrence of treatment-emergent adverse events was also similar between BIF and degludec. CONCLUSIONS: Once-weekly BIF achieved excellent glycemic control similar to degludec, with no concerning hypoglycemia or other safety findings.

Novel Once-Weekly Basal Insulin Fc Achieved Similar Glycemic Control With a Safety Profile Comparable to Insulin Degludec in Patients With Type 1 Diabetes.

OBJECTIVE: Basal Insulin Fc (BIF; insulin efsitora alfa; LY3209590), a fusion protein combining a novel single-chain insulin variant with a human IgG Fc domain, is designed for once-weekly basal insulin administration. This phase 2 study assessed safety and efficacy of BIF versus degludec in 265 patients with type 1 diabetes (T1D) using multiple daily injections. RESEARCH DESIGN AND METHODS: During this randomized, parallel, open-label study, patients with T1D were randomized (1:1) to receive BIF once weekly or degludec once daily over the 26-week treatment period. Both groups were titrated to a fasting glucose level of 80-100 mg/dL. The primary end point was HbA1c change from baseline to week 26 (noninferiority margin, 0.4%). Secondary end points included percent time in range (TIR) (70-180 mg/dL), continuous glucose monitoring (CGM) fasting glucose (FG) level, and rate of hypoglycemia. RESULTS: After 26 weeks, patients receiving BIF had noninferior HbA1c change from baseline versus those receiving degludec, with a statistically significant treatment difference of 0.17% (90% CI 0.01, 0.32; P = 0.07) favoring the comparator. Percent TIR was similar for patients in the BIF (56.1%) and degludec (58.9%; P = 0.112) groups at week 26. FG values were significantly higher for patients receiving BIF (158.8 mg/dL) versus degludec (143.2 mg/dL; P = 0.003). Rates of CGM-derived hypoglycemia were not statistically significantly different for BIF and degludec over 24 h for level 1 (P = 0.960) or level 2 (P = 0.517) hypoglycemia during the treatment period. Occurrence of serious adverse events was similar between the BIF and degludec groups. CONCLUSIONS: Once-weekly BIF demonstrated noninferior glycemic control to once-daily degludec (treatment difference: 0.17% favoring degludec) and no difference in hypoglycemia or other safety findings in patients with T1D.

Pharmacokinetics, Efficacy, and Safety of a SARS-CoV-2 Antibody Treatment in Pediatric Participants: An Open-Label Addendum of a Placebo-Controlled, Randomized Phase 2/3 Trial.

INTRODUCTION: Bamlanivimab and etesevimab (BAM + ETE) are monoclonal antibodies (mAbs) effective in reducing COVID-19-related hospitalizations and all-cause mortality in adult participants at increased risk for severe disease. We present pharmacokinetic (PK), efficacy, and safety results from pediatric participants (< 18 years of age) with COVID-19 who were treated with BAM + ETE. METHODS: In an addendum to the phase 2/3 BLAZE-1 clinical trial (NCT04427501), pediatric participants received open-label weight-based dosing (WBD, n = 94) based on exposure-matching to the authorized dose of BAM + ETE in adult participants. For efficacy and safety assessments, placebo (n = 14) and BAM + ETE (n = 20)-treated adolescent participants (> 12 to < 18 years of age) from the BLAZE-1 trial were included in the overall pediatric population (N = 128). All participants had mild to moderate COVID-19 upon enrollment and ≥ 1 risk factor for severe COVID-19. The primary objective was to characterize the PK of BAM and ETE in the WBD population. RESULTS: The median age of the participants was 11.2 years, 46.1% were female, 57.9% were Black/African American, and 19.7% were Hispanic/Latino. The area under the curve for BAM and ETE in the WBD population was similar to that previously observed in adults. There were no COVID-19-related hospitalizations or deaths. All adverse events (AE) except one were mild or moderate, with one participant reporting a serious AE. CONCLUSION: WBD in pediatric participants achieved similar drug exposures compared to adult participants that received the authorized BAM + ETE dose. The pediatric efficacy and safety data were consistent with adults receiving mAbs for COVID-19. TRIAL REGISTRATION NUMBER: NCT04427501.

PK/PD modeling links accelerated resolution of COVID-19-related clinical symptoms to SARS-CoV-2 viral load reduction in patients following treatment with Bamlanivimab alone or Bamlanivimab and Etesevimab together.

The relationship between severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) viral load reduction and disease symptom resolution remains largely undefined for coronavirus disease 2019 (COVID-19). While the vaccine-derived immunity takes time to develop, neutralizing monoclonal antibodies offer immediate, passive immunity to patients with COVID-19. Bamlanivimab and etesevimab are two potent neutralizing monoclonal antibodies directed to the receptor binding domain of the spike protein of SARS-CoV-2. This study aims to describe the relationship between viral load and resolution of eight common COVID-19-related symptoms in patients following treatment with neutralizing monoclonal antibodies (bamlanivimab alone or bamlanivimab and etesevimab together), in a phase II clinical trial. Corresponding pharmacokinetics (PKs), viral load, and COVID-19-related symptom data were modeled using Nonlinear Mixed Effects Modeling to describe the time-course of eight COVID-19-related symptoms in an ordered categorical manner (none, mild, moderate, and severe), following administration of bamlanivimab or bamlanivimab and etesevimab together to participants with COVID-19. The PK/pharmacodynamic (PD) models characterized the exposure-viral load-symptom time course of the eight preselected COVID-19-related symptoms. Baseline viral load (BVL), change in viral load from baseline, and time since the onset of symptoms, demonstrated statistically significant effects on symptom score probabilities. Higher BVL generally indicated an increased probability of symptom severity. The severity of symptoms decreased over time, partially driven by the decrease in viral load. The effect of increasing time resulting in decreased severity of symptoms was over and above the effect of decreasing viral load. Administration of bamlanivimab alone or together with etesevimab results in a faster time to resolution of COVID-19-related symptoms compared to placebo.

Optimization of trial duration to predict long-term HbA1c change with therapy: A pharmacometrics simulation-based evaluation.

Glycated hemoglobin (HbA1c) is the main biomarker of diabetes drug development. However, because of its delayed turnover, trial duration is rarely shorter than 12 weeks, and being able to predict long-term HbA1c with precision using data from shorter studies would be beneficial. The feasibility of reducing study duration was therefore investigated in this study, assuming a model-based analysis. The aim was to investigate the predictive performance of 24- and 52-week extrapolations using data from up to 4, 6, 8 or 12 weeks, with six previously published pharmacometric models of HbA1c. Predictive performance was assessed through simulation-based dose-response predictions and model averaging (MA) with two hypothetical drugs. Results were consistent across the methods of assessment, with MA supporting the results derived from the model-based framework. The models using mean plasma glucose (MPG) or nonlinear fasting plasma glucose (FPG) effect, driving the HbA1c formation, showed good predictive performance despite a reduced study duration. The models, using the linear effect of FPG to drive the HbA1c formation, were sensitive to the limited amount of data in the shorter studies. The MA with bootstrap demonstrated strongly that a 4-week study duration is insufficient for precise predictions of all models. Our findings suggest that if data are analyzed with a pharmacometric model with MPG or FPG with a nonlinear effect to drive HbA1c formation, a study duration of 8 weeks is sufficient with maintained accuracy and precision of dose-response predictions.

A Quantitative Modeling and Simulation Framework to Support Candidate and Dose Selection of Anti-SARS-CoV-2 Monoclonal Antibodies to Advance Bamlanivimab Into a First-in-Human Clinical Trial.

Neutralizing monoclonal antibodies (mAb), novel therapeutics for the treatment of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), have been urgently researched from the start of the pandemic. The selection of the optimal mAb candidate and therapeutic dose were expedited using open-access in silico models. The maximally effective therapeutic mAb dose was determined through two approaches; both expanded on innovative, open-science initiatives. A physiologically-based pharmacokinetic (PBPK) model, incorporating physicochemical properties predictive of mAb clearance and tissue distribution, was used to estimate mAb exposure that maintained concentrations above 90% inhibitory concentration of in vitro neutralization in lung tissue for up to 4 weeks in 90% of patients. To achieve fastest viral clearance following onset of symptoms, a longitudinal SARS-CoV-2 viral dynamic model was applied to estimate viral clearance as a function of drug concentration and dose. The PBPK model-based approach suggested that a clinical dose between 175 and 500 mg of bamlanivimab would maintain target mAb concentrations in the lung tissue over 28 days in 90% of patients. The viral dynamic model suggested a 700 mg dose would achieve maximum viral elimination. Taken together, the first-in-human trial (NCT04411628) conservatively proceeded with a starting therapeutic dose of 700 mg and escalated to higher doses to evaluate the upper limit of safety and tolerability. Availability of open-access codes and application of novel in silico model-based approaches supported the selection of bamlanivimab and identified the lowest dose evaluated in this study that was expected to result in the maximum therapeutic effect before the first-in-human clinical trial.

Population Pharmacokinetics and Pharmacodynamics of the Neutralizing Antibodies Bamlanivimab and Etesevimab in Patients With Mild to Moderate COVID-19 Infection.

Bamlanivimab and etesevimab are neutralizing antibodies indicated for treatment of coronavirus disease 2019 (COVID-19) in patients with early mild or moderate disease. We present the use of pharmacokinetic/pharmacodynamic (PK/PD) modeling that characterizes the timecourse of viral load obtained from 2,970 patients from 2 phase II clinical trials. The model was used for identification of optimal doses that would result in at least 90% of patients achieving serum drug concentrations that result in 90% of maximum drug effect (IC90) for at least 28 days. The serum IC90 (95% confidence interval) was estimated to be 4.2 (3.2-4.3) µg/mL for bamlanivimab and 12.6 (9.7-12.8) µg/mL for etesevimab. Observed clinical trial data confirmed PK and PK/PD model predictions that doses of 700 mg bamlanivimab and 1,400 mg etesevimab would result in maximum reduction in viral load, with no additional effect seen at higher doses. No dose adjustment is recommended as age, sex, race, baseline viral load, and hepatic impairment did not have a significant impact on the PK of the antibodies. Earlier drug administration resulted in greater reductions in viral load, demonstrating the importance of receiving treatment as soon as possible. Relative to placebo, typical reduction in viral load over a 7-day period was estimated to be 80 or 93% (drug administered 4 days or 1 day after the onset of symptoms, respectively), P < 0.0001. PK/PD modeling and simulation was pivotal throughout the drug development and emergency use authorization process.

Development and Verification of a Body Weight-Directed Disease Trial Model for Glucose Homeostasis.

Weight loss has been associated with improvement in insulin sensitivity. It is consequently a cornerstone in the management of type 2 diabetes mellitus (T2DM). However, the strictly quantitative relationship between weight loss, insulin sensitivity, and clinically relevant glucose homeostasis biomarkers as well as changes therein as T2DM progresses is not yet fully understood. Therefore, the objective of our research was to establish a body weight-directed disease trial model for glucose homeostasis. To that end, we conducted a model-based meta-analysis using time course data of body weight loss (following lifestyle change or surgical procedure) and corresponding improvement of insulin sensitivity expressed as the Matsuda index. Changes in body weight were best described by a sigmoidal Emax model, whereas changes in the Matsuda index were best described by a linear model with a slope of 3.49. Once developed and verified, the model-based meta-analysis was linked to a disease-drug trial model for T2DM previously developed by our group to characterize and predict the impact of weight loss on clinically relevant glucose homeostasis biomarkers. The joint model was then used to conduct clinical trial simulations, which showed that weight loss can greatly improve clinically relevant glucose homeostasis biomarkers in T2DM patients.

First-in-Human Study of Bamlanivimab in a Randomized Trial of Hospitalized Patients With COVID-19.

Therapeutics for patients hospitalized with coronavirus disease 2019 (COVID-19) are urgently needed during the pandemic. Bamlanivimab is a potent neutralizing monoclonal antibody that blocks severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) attachment and entry into human cells, which could potentially lead to therapeutic benefit. J2W-MC-PYAA was a randomized, double-blind, sponsor unblinded, placebo-controlled, single ascending dose first-in-human trial (NCT04411628) in hospitalized patients with COVID-19. A total of 24 patients received either placebo or a single dose of bamlanivimab (700 mg, 2,800 mg, or 7,000 mg). The primary objective was assessment of safety and tolerability, including adverse events and serious adverse events, with secondary objectives of pharmacokinetic (PK) and pharmacodynamic analyses. Treatment-emergent adverse event (TEAE) rates were identical in the placebo and pooled bamlanivimab groups (66.7%). There were no apparent dose-related increases in the number or severity of TEAEs. There were no serious adverse events or deaths during the study, and no discontinuations due to adverse events. PKs of bamlanivimab is linear and exposure increased proportionally with dose following single i.v. administration. The half-life was ~ 17 days. These results demonstrate the favorable safety profile of bamlanivimab, and provided the initial critical evaluation of safety, tolerability, and PKs in support of the development of bamlanivimab in several ongoing clinical trials.

Desipramine, substrate for CYP2D6 activity: population pharmacokinetic model and design elements of drug-drug interaction trials.

AIMS: To develop a population pharmacokinetic model to describe the pharmacokinetics of desipramine in healthy subjects, after oral administration of a 50mg dose. Additional objectives were to develop a semi-mechanistic population pharmacokinetic model for desipramine, which allowed simulation of CYP2D6-mediated inhibition, when using desipramine as a probe substrate, and to evaluate certain study design elements, such as duration of desipramine pharmacokinetic sampling, required sample size and optimal pharmacokinetic sampling schedule for intermediate, extensive and ultrarapid metabolizers of CYP2D6 substrates. RESULTS: The mean population estimates of the first order absorption rate constant (k(a) ), apparent clearance (CL/F) and apparent volume of distribution at steady state (V(ss) /F) were 0.15h(-1) , 111 lh(-1) and 2950 l, respectively. Further, using the proposed semi-mechanistic hepatic intrinsic clearance model with Bayesian inference, mean population desipramine hepatic intrinsic clearance was estimated to be 262 lh(-1) with between-subject variability of 84%. d-optimal PK sampling times for intermediate metabolizers were calculated to be approximately 0.25, 24, 75 and 200h. Similar sampling times were found for ultrarapid and extensive metabolizers except that the second d-optimal sample was earlier at 14 and 19h, respectively, compared with 24h for intermediate metabolizers. This difference in sampling times between the three genotypes can be attributed to the different intrinsic clearances and elimination rates. CONCLUSIONS: A two compartment population pharmacokinetic model best described desipramine disposition. The semi-mechanistic population model developed is suitable to describe the pharmacokinetic behaviour of desipramine for the dose routinely used in drug-drug interaction (DDI) studies. Based on this meta-analysis of seven trials, a sample size of 21 subjects in cross-over design is appropriate for assessing CYP2D6 interaction with novel compounds.

A new probabilistic rule for drug-dug interaction prediction.

An innovative probabilistic rule is proposed to predict the clinical significance or clinical insignificance of DDI. This rule is coupled with a hierarchical Bayesian model approach to summarize substrate/inhibitor's PK models from multiple published resources. This approach incorporates between-subject and between-study variances into DDI prediction. Hence, it can predict both population-average and subject-specific AUCR. The clinically significant DDI, weak DDI, and clinically insignificant inhibitions are decided by the probabilities of predicted AUCR falling into three intervals, (-infinity, 1.25), (1.25, 2), and (2, infinity). The main advantage of this probabilistic rule to predict clinical significance of DDI over the deterministic rule is that the probabilistic rule considers the sample variability, and the decision is independent of sampling variation; while deterministic rule based decision will vary from sample to sample. The probabilistic rule proposed in this paper is best suited for the situation when in vivo PK studies and models are available for both the inhibitor and substrate. An early decision on clinically significant or clinically insignificant inhibition can avoid additional DDI studies. Ketoconazole and midazolam are used as an interaction pair to illustrate our idea. AUCR predictions incorporating between-subject variability always have greater variances than population-average AUCR predictions. A clinically insignificant AUCR at population-average level is not necessarily true when considering between-subject variability. Additional simulation studies suggest that predicted AUCRs highly depend on the interaction constant K(i) and dose combinations.

Development and Qualification of a Drug-Disease Modeling Platform to Characterize Clinically Relevant Endpoints in Type 2 Diabetes Trials.

Type 2 diabetes mellitus (T2DM) is a chronic, progressive disease characterized by persistently elevated blood glucose concentration (hyperglycemia). We developed a mechanistic drug-disease modeling platform based on data from more than 4,000 T2DM subjects in seven phase II/III clinical trials. The model integrates longitudinal changes in clinically relevant biomarkers of glycemic control with information on baseline disease state, demographics, disease progression, and different therapeutic interventions, either when given alone or as add-on combination therapy. The model was able to simultaneously characterize changes in fasting plasma glucose, fasting serum insulin, and glycated hemoglobin A1c following administration of sulfonylurea, metformin, and thiazolidinedione as well as disease progression in clinical trials ranging from 16-104 weeks of treatment. The mechanistic components of this generalized mechanism-based platform, based on knowledge of pharmacology, insulin-glucose homeostatic feedback, and diabetes pathophysiology, allows its application to be further expanded to other antidiabetic drug classes and combination therapies.

Simulation-Based Evaluation of Dose-Titration Algorithms for Rapid-Acting Insulin in Subjects with Type 2 Diabetes Mellitus Inadequately Controlled on Basal Insulin and Oral Antihyperglycemic Medications.

BACKGROUND: The purpose of this prospective, model-based simulation approach was to evaluate the impact of various rapid-acting mealtime insulin dose-titration algorithms on glycemic control (hemoglobin A1c [HbA1c]). METHODS: Seven stepwise, glucose-driven insulin dose-titration algorithms were evaluated with a model-based simulation approach by using insulin lispro. Pre-meal blood glucose readings were used to adjust insulin lispro doses. Two control dosing algorithms were included for comparison: no insulin lispro (basal insulin+metformin only) or insulin lispro with fixed doses without titration. RESULTS: Of the seven dosing algorithms assessed, daily adjustment of insulin lispro dose, when glucose targets were met at pre-breakfast, pre-lunch, and pre-dinner, sequentially, demonstrated greater HbA1c reduction at 24 weeks, compared with the other dosing algorithms. Hypoglycemic rates were comparable among the dosing algorithms except for higher rates with the insulin lispro fixed-dose scenario (no titration), as expected. The inferior HbA1c response for the "basal plus metformin only" arm supports the additional glycemic benefit with prandial insulin lispro. CONCLUSIONS: Our model-based simulations support a simplified dosing algorithm that does not include carbohydrate counting, but that includes glucose targets for daily dose adjustment to maintain glycemic control with a low risk of hypoglycemia.

A Comprehensive Review of Novel Drug-Disease Models in Diabetes Drug Development.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, which affects millions of people worldwide. The disease is characterized by chronically elevated blood glucose concentrations (hyperglycaemia), which result in comorbidities and multi-organ dysfunction. This is due to a gradual loss of glycaemic control as a result of increasing insulin resistance, as well as decreasing ß-cell function. The objective of T2DM drug interventions is, therefore, to reduce fasting and postprandial blood glucose concentrations to normal, healthy levels without hypoglycaemia. Several classes of novel antihyperglycaemic drugs with various mechanisms of action have been developed over the past decades or are currently under clinical development. The development of these drugs is routinely supported by the application of pharmacokinetic/pharmacodynamic modelling and simulation approaches. They integrate information on the drug's pharmacokinetics, clinically relevant biomarker information and disease progression into a single, unifying approach, which can be used to inform clinical study design, dose selection and drug labelling. The objective of this review is to provide a comprehensive overview of the quantitative approaches that have been reported since the 2008 review by Landersdorfer and Jusko in an increasing order of complexity, starting with glucose homeostasis models. Each of the presented approaches is discussed with respect to its strengths and limitations, and respective knowledge gaps are highlighted as potential opportunities for future drug-disease model development in the area of T2DM.

Clinical Pharmacokinetics of Dulaglutide in Patients with Type 2 Diabetes: Analyses of Data from Clinical Trials.

BACKGROUND AND OBJECTIVE: Dulaglutide is a long-acting glucagon-like peptide-1 receptor agonist administered as once-weekly subcutaneous injections for the treatment of type 2 diabetes (T2D). The clinical pharmacokinetics of dulaglutide were characterized in patients with T2D and healthy subjects. METHODS: The pharmacokinetics of dulaglutide were assessed throughout clinical development, including conventional pharmacokinetic analysis in clinical pharmacology studies and population pharmacokinetic analyses of data from combined phase 2 and phase 3 studies in patients with T2D. The effects of potential covariates on dulaglutide population pharmacokinetics were evaluated using nonlinear mixed-effects models. RESULTS: Dulaglutide gradually reached the maximum concentration in 48 h and had a terminal elimination half-life of 5 days. Steady state was achieved between the second and fourth doses. The accumulation ratio was 1.56 for the 1.5 mg dose. Intra-individual variability estimates for the area under the plasma concentration-time curve and the maximum concentration were both <17% [coefficient of variation (CV)]. There was no difference in pharmacokinetics between injection sites (arm, thigh or abdomen). Dulaglutide pharmacokinetics were well described by a two-compartment model with first-order absorption and elimination. The population clearance was estimated at 0.126 L/h [inter-individual variability (CV) 33.8%]. Age, body weight, sex, race and ethnicity did not influence dulaglutide pharmacokinetics to any clinically relevant degree. CONCLUSION: The pharmacokinetics of dulaglutide support once-weekly administration in patients with T2D. The pharmacokinetic findings suggest that dose adjustment is not necessary on the basis of body weight, sex, age, race or ethnicity or site of injection.