Training the next generation of pharmacometric modelers: a multisector perspective.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

A population pharmacokinetic-pharmacodynamic model evaluating efficacy of nalbuphine extended-release in patients with prurigo nodularis.

AIMS: Population pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PK-PD) models were used to describe the exposure-response (E-R) relationship between nalbuphine exposure and two widely used rating scales for itch: the Numerical Rating Scale for the subject's 'average'; itch experience (NRS-AV) and the Worst Itch (WI-NRS), with 24-h recall. Simulations based on the model E-R relationship were used to support dose selection for Phase 3 clinical trials and were evaluated with a target of reducing the 7-day average of the 24-h WI-NRS by at least 30% from baseline in most of the analysis population. METHODS: Data from two clinical trials (NCT02373215: 9 healthy subjects; NCT02174419: 62 subjects with PN), in patients with prurigo nodularis (PN) with moderate to severe itch who received treatment with either of two doses of nalbuphine extended release (ER) or placebo, were used for the analysis. A two-compartment PK model with serial zero and first-order oral absorption was used to describe drug exposure. A maximum effect ( E max ) model with a placebo effect was used to model the itch response endpoints (NRS-AV, WI-NRS). RESULTS: The PK-PD model predicted the exposure-related reduction in both NRS-AV and WI-NRS over time with approximately 63% and 27% of E max , respectively. Exposures associated with 80% of E max were achieved in about 78% of the patients at 162 mg, twice daily (BID), compared to 35% at 81 mg BID. CONCLUSION: Simulated dose response indicated that 108 and 162 mg BID doses result in the highest proportion of patients achieving at least a 30% reduction in NRS-AV and WI-NRS, respectively.

Impact of Drug Exposure on Resistance Selection Following Artemether-Lumefantrine Treatment for Malaria in Children With and Without HIV in Uganda.

Artemisinin-based combination therapies (ACTs) are the primary treatment for malaria. It is essential to characterize the pharmacokinetics (PKs) and pharmacodynamics (PDs) of ACTs in vulnerable populations at risk of suboptimal dosing. We developed a population PK/PD model using data from our previous study of artemether-lumefantrine in HIV-uninfected and HIV-infected children living in a high-transmission region of Uganda. HIV-infected children were on efavirenz-, nevirapine-, or lopinavir-ritonavir-based antiretroviral regimens, with daily trimethoprim-sulfamethoxazole prophylaxis. We assessed selection for resistance in two key parasite transporters, pfcrt and pfmdr1, over 42-day follow-up and incorporated genotyping into a time-to-event model to ascertain how resistance genotype in relation to drug exposure impacts recurrence risk. Two hundred seventy-seven children contributed 364 episodes to the model (186 HIV-uninfected and 178 HIV-infected), with recurrent microscopy-detectable parasitemia detected in 176 episodes by day 42. The final model was a two-compartment model with first-order absorption and an estimated age effect on bioavailability. Systemic lumefantrine exposure was highest with lopinavir-ritonavir, lowest with efavirenz, and equivalent with nevirapine and HIV-uninfected children. HIV status and lumefantrine concentration were significant factors associated with recurrence risk. Significant selection was demonstrated for pfmdr1 N86 and pfcrt K76 in recurrent infections, with no evidence of selection for pfmdr1 Y184F. Less sensitive parasites were able to tolerate lumefantrine concentrations ~ 3.5-fold higher than more sensitive parasites. This is the first population PK model of lumefantrine in HIV-infected children and demonstrates selection for reduced lumefantrine susceptibility, a concern as we confront the threat to ACTs posed by emerging artemisinin resistance in Africa.

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.

A model-based analysis to guide gonadotropin-releasing hormone receptor antagonist use for management of endometriosis.

AIMS: To identify linzagolix doses, an oral GnRH receptor antagonist, that effectively lower oestradiol (E2) to relieve endometriosis-related pelvic pain without compromising bone health. METHODS: Integrated statistical, pharmacokinetic-pharmacodynamic and systems pharmacology models were developed from Phase 1 and 2 clinical trial data in healthy volunteers and patients, receiving linzagolix 25-200 mg daily or placebo, and analysed simultaneously. The main outcome measures were pelvic pain scores for dysmenorrhoea, nonmenstrual pelvic pain (NMPP), uterine bleeding and lumbar spine bone mineral density (BMD). RESULTS: Linzagolix pharmacokinetics were described by a 2-compartment model with sequential zero/first-order absorption process (CL/F: 0.422 L/h). E2 changes over time were well described as a function of linzagolix 24-hour AUC (AUC50 : 1.68 × 105 ng h/mL). For a Caucasian reference patient, a change in E2 from 50-20 pg/mL at 24 weeks increased the odds of relief of dysmenorrhoea 1.33-fold and NMPP 1.07-fold (95% CI: 1.22-1.47 and 1.02-1.12, respectively) and decreased bleeding days by 1.55 (95% CI: 1.39-1.72). A previously validated quantitative systems pharmacology BMD model was adjusted to the clinical data. The mean week 24 lumbar spine BMD change from baseline ranged from -0.092% in the 50 mg dose, -1.30% in the 100 mg dose group and -2.67% in the 200 mg dose group. DISCUSSION: The previously-reported E2 target range (20-50 pg/mL) to balance efficacy and safety endpoints was confirmed. Linzagolix once daily doses between 75-125 mg daily were expected to meet endometriosis-associated pain, efficacy, and BMD loss targets in Caucasian patients.

A Model-Informed Drug Development (MIDD) Approach for a Low Dose of Empagliflozin in Patients with Type 1 Diabetes.

In clinical trials, sodium-glucose co-transporter (SGLT) inhibitor use as adjunct to insulin therapy in type 1 diabetes (T1D) provides glucometabolic benefits while diabetic ketoacidosis risk is increased. The SGLT2 inhibitor empagliflozin was evaluated in two phase III trials: EASE-2 and EASE-3. A low, 2.5-mg dose was included in EASE-3 only. As the efficacy of higher empagliflozin doses (i.e., 10 and 25 mg) in T1D has been established in EASE-2 and EASE-3, a modeling and simulation approach was used to generate additional supportive evidence on efficacy for the 2.5-mg dose. We present the methodology behind the development and validation of two modeling and simulation frameworks: M-EASE-1, a semi-mechanistic model integrating information on insulin, glucose, and glycated hemoglobin; and M-EASE-2, a descriptive model informed by prior information. Both models were developed independently of data from EASE-3. Simulations based on these models assessed efficacy in untested clinical trial scenarios. In this manner, the models provide supportive evidence for efficacy of low-dose empagliflozin 2.5 mg in patients with T1D, illustrating how pharmacometric analyses can support efficacy assessments in the context of limited data.

The role of 2-hydroxyglutarate magnetic resonance spectroscopy for the determination of isocitrate dehydrogenase status in lower grade gliomas versus glioblastoma: a systematic review and meta-analysis of diagnostic test accuracy.

PURPOSE: Magnetic resonance spectroscopy (MRS) provides a non-invasive means of determining isocitrate dehydrogenase (IDH) status. Determination of 2-hydroxyglutarate (2-HG) presence through MRS is a means of determining IDH status; however, differences may be seen by grade. The goal of this paper is to perform a diagnostic test accuracy (DTA) meta-analysis on 2-HG MRS for IDH status in both lower-grade glioma (LGG) and glioblastoma (GBM) in preoperative patients. METHODS: A systematic review and meta-analysis were performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Diagnostic Test Accuracy guidelines. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies 2. The search was up to date as of 09/02/2021. Nine English-language journal articles were included. RESULTS: The meta-analysis found a pooled sensitivity of 93% (95% CI 58-99%) and specificity of 84% (95% CI 51-96%) for LGG (n= 181). For GBM (n= 77), the pooled sensitivity was 84% (95% CI 25.0-99%) and the specificity was 97% (95% CI 43-100%). CONCLUSION: 2-HG MRS shows promise as a non-invasive means of determining IDH status, with specificity higher for GBM and sensitivity higher for LGG. The wide confidence intervals are notable, however, in particular related to the small number of IDH-mutant GBM studied. Diagnostic heterogeneity was particularly present for LGG, and the hierarchical summary receiver operator curves showed poor predictive accuracy in both groups. For more conclusive results, diagnostic test accuracy statistics need to be quantified with larger studies and more deliberate study design.

Quantification of the Impact of Partition Coefficient Prediction Methods on Physiologically Based Pharmacokinetic Model Output Using a Standardized Tissue Composition.

Tissue:plasma partition coefficients are key parameters in physiologically based pharmacokinetic (PBPK) models, yet the coefficients are challenging to measure in vivo. Several mechanistic-based equations have been developed to predict partition coefficients using tissue composition information and the compound's physicochemical properties, but it is not clear which, if any, of the methods is most appropriate under given circumstances. Complicating the evaluation, each prediction method was developed, and is typically employed, using a different set of tissue composition information, thereby making a controlled comparison impossible. This study proposed a standardized tissue composition for humans that can be used as a common input for each of the five frequently used prediction methods. These methods were implemented in R and were used to predict partition coefficients for 11 drugs, classified as strong bases, weak bases, acids, neutrals, and zwitterions. PBPK models developed in R (mrgsolve) for each drug and each set of partition coefficient predictions were compared with respective observed plasma concentration data. Percent root mean square error and half-life percent error were used to evaluate the accuracy of the PBPK model predictions using each partition coefficient method as summarized by strong bases, weak bases, acids, neutrals, and zwitterions characterization. The analysis indicated that no partition coefficient method consistently yielded the most accurate PBPK model predictions. As such, PBPK model predictions using all partition coefficient methods should be considered during drug development. SIGNIFICANCE STATEMENT: Several mechanistic-based methods exist to predict tissue:plasma partition coefficients critical to PBPK modeling. Controlled comparisons are confounded by the use of different tissue composition values for each method; a standardized tissue composition was proposed. Resulting assessments indicated that no method was consistently superior; therefore, sensitivity of PBPK predictions to each method may be warranted prior to model optimization.

A Systematic Review of Large Agriculture Vehicles Use and Crash Incidents on Public Roads.

Background: Agricultural vehicles are a common sight on rural public roads. However, due to their larger mass (height, width, length, and weight), there are concerns about safety. The aim of this paper is to explore crash incidents on public roads of agricultural vehicles to determine the size of the problem, risk factors, and potential prevention strategies.Methods: A systematic review using the PRISMA guidelines was undertaken of peer-reviewed literature from Medline, Agricola, Scopus, PsycInfo, Science Direct, Web of Science, and SafetyLit. Crash incident rates, risk factors, and prevention strategies were extracted from the articles, and a review of quality was undertaken using McMasters guidelines.Results: Included in the review were 30 articles, with the majority from the United States. Crash risk rates, where reported, were low relative to agricultural vehicle use and when compared to overall road crash numbers. Crash risk factors included weather and visibility, age, personal and driving characteristics, road conditions, and event characteristics. Prevention strategies proposed were targeted at drivers and operators, vehicles, road design, driving behavior, and surveillance, policy, and technology.Conclusions: Overall, reported crash numbers involving large agricultural vehicles were low. Currently, there is limited capacity to calculate exposure rates compounded by the difficulties in identifying road incidents that involve agriculture vehicles. Better surveillance systems are required to improve our understanding of exposure and crash incident rates. Future research into the multiplicity of interrelated factors involved in agriculture vehicle crashes on roads, exposure rates, and evidence for the effectiveness of the prevention strategies is required.

Toward Progress in Quantitative Translational Medicine: A Call to Action.

Quantitative translational medicine (QTM) is envisioned as a multifaceted discipline that will galvanize the path from idea to medicine through quantitative translation across the discovery, development, regulatory, and utilization spectrum. Here, we summarize results of an American Society for Clinical Pharmacology and Therapeutics (ASCPT) survey on barriers relevant to the advancement of QTM and propose opportunities for its deployment. Importantly, we offer a call to action to break down these barriers through patient-centered stewardship, effective communication, cross-sector collaboration, and a modernized educational curriculum.

Low-dose empagliflozin as adjunct-to-insulin therapy in type 1 diabetes: A valid modelling and simulation analysis to confirm efficacy.

AIM: To confirm the observed reduction in HbA1c for the 2.5 mg dose in EASE-3 by modelling and simulation analyses. MATERIALS AND METHODS: Independent of data from EASE-3 that tested 2.5 mg, we simulated the effect of a 2.5 mg dose through patient-level, exposure-response modelling in the EASE-2 clinical study. A primary semi-mechanistic model evaluated efficacy considering clinical insulin dose adjustments made after treatment initiation that potentially limited HbA1c reductions. The model was informed by pharmacokinetic, insulin dose, mean daily glucose and HbA1c data, and was verified by comparing the simulations with the observed HbA1c change in EASE-3. One of two empagliflozin phase 3 trials in type 1 diabetes (EASE-3 but not EASE-2) included a lower 2.5 mg dose. A placebo-corrected HbA1c reduction of 0.28% was demonstrated without the increased risk of diabetic ketoacidosis observed at higher doses (10 mg and 25 mg). Since only one trial included the lower dose, we aimed to confirm the observed reduction in HbA1c for the 2.5 mg dose by modelling and simulation analyses. RESULTS: The simulated 26-week mean HbA1c change was -0.41% without insulin dose adjustment and -0.29% at 26 weeks with insulin dose adjustment. A simplified (descriptive) model excluding insulin dose and mean daily glucose confirmed the -0.29% HbA1c change that would have been observed had the EASE-2 population received a 2.5 mg dose for 26/52 weeks. CONCLUSIONS: The HbA1c benefit of low-dose empagliflozin directly observed in the EASE-3 trial was confirmed by two modelling and simulation approaches.

Quantitative Systems Pharmacology and Physiologically-Based Pharmacokinetic Modeling With mrgsolve: A Hands-On Tutorial.

mrgsolve is an open-source R package available on the Comprehensive R Archive Network. It combines R and C++ coding for simulation from hierarchical, ordinary differential equation-based models. Its efficient simulation engine and integration into a parallelizable, R-based workflow makes mrgsolve a convenient tool both for simple and complex models and thus is ideal for physiologically-based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) model. This tutorial will first introduce the basics of the mrgsolve simulation workflow, including model specification, the introduction of interventions (dosing events) into the simulation, and simulated results postprocessing. An applied simulation example is then presented using a PBPK model for voriconazole, including a model validation step against adult and pediatric data sets. A final simulation example is then presented using a previously published QSP model for mitogen-activated protein kinase signaling in colorectal cancer, illustrating population simulation of different combination therapies.

Cardiopulmonary resuscitation (CPR) psychomotor skills of laypeople, as affected by training interventions, number of times trained and retention testing intervals: A dataset derived from a systematic review.

This article is a companion to a systematic review, entitled, Associations between cardiopulmonary resuscitation (CPR) knowledge, self-efficacy, training history and willingness to perform CPR and CPR psychomotor skills: a systematic review (Riggs et al., 2019). The data tables described in this article summarise the impact that specific training interventions, number of times trained, and retention testing intervals have on laypeople's CPR psychomotor skills, as reported by peer-reviewed journal articles. The psychomotor skills included are: compression rate, compression depth, duration of interruptions to compressions, chest recoil, hand placement, proportion of adequate or 'correct' compressions, ventilation volume, compression-to-ventilation ratio, duty cycle and overall skills. The data tables described in this article are available as a supplementary file to this article.

Pharmacokinetics of trastuzumab emtansine (T-DM1) as a single agent or in combination with pertuzumab in HER2-positive breast cancer patients with recurrent or locally advanced metastatic breast cancer.

PURPOSE: The phase III MARIANNE study investigated single-agent trastuzumab emtansine (T-DM1) and combination T-DM1 plus pertuzumab as the first-line treatment for human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (MBC). Pharmacokinetic properties of T-DM1 and pertuzumab in these patients and the potential for drug-drug interactions (DDIs) were assessed. METHODS: Pharmacokinetic samples of T-DM1-related analytes (T-DM1 conjugate, total trastuzumab, DM1) and pertuzumab were analyzed. Observed pharmacokinetic data were summarized for all analytes. Historical population pharmacokinetic models for T-DM1 conjugate and pertuzumab in HER2-positive MBC were used to derive empirical Bayes estimates of pharmacokinetic parameters. RESULTS: In MARIANNE (N = 375), mean ± standard deviation population pharmacokinetic model-predicted Cycle 1 Cmax for T-DM1 conjugate was 74.4 ± 10.1 µg/mL, Cycle 1 Ctrough was 1.34 ± 0.802 µg/mL, and area under the concentration-time curve from time zero to infinity after first dose (AUCinf) was 338 ± 69.5 µg*day/mL. These values were similar to other T-DM1 studies. Pharmacokinetics of T-DM1 conjugate and other analytes (total trastuzumab, DM1) were similar with or without pertuzumab. In the pertuzumab plus T-DM1 arm, mean model-predicted Cycle 1 pertuzumab Cmax, Ctrough, and AUCinf were 276 ± 50.0 µg/mL, 64.8 ± 17.9 µg/mL, and 4470 ± 1360 µg*day/mL, respectively. These values were similar to other pertuzumab studies. CONCLUSIONS: Based on the population pharmacokinetic analysis of T-DM1 conjugate and pertuzumab, pharmacokinetics are similar across different lines of treatment and stages of disease including previously untreated MBC patients, and no DDIs were identified for combined use of T-DM1 and pertuzumab.

Associations between cardiopulmonary resuscitation (CPR) knowledge, self-efficacy, training history and willingness to perform CPR and CPR psychomotor skills: A systematic review.

AIM: To determine whether training history (including number of times and duration since last training), knowledge, self-efficacy or willingness are associated with cardiopulmonary resuscitation (CPR) psychomotor skills. METHODS: Eight databases were systematically searched from January 2005 to February 2018 for articles that involved adult layperson participants and explored an association between training history, knowledge, self-efficacy or willingness and CPR psychomotor skills or survival outcomes after real CPR attempts. RESULTS: Thirty-four articles with a total of 35,421 participants were included. CPR training was found to improve psychomotor skills, compared to no training, and any previous training was associated with better skills, compared to no previous training, however only the use of a popular song promoted meaningful retention of a specifically targeted skill, compared to standard training methods. Skills deteriorated within 3 months, then plateaued from 3 to 6 months. Self-efficacy was weakly associated with skill level, however knowledge was not associated with skill level. No studies assessed the association between willingness and psychomotor skills. CONCLUSION: All laypeople should attend an instructor-led CPR training session with real-time or delayed feedback to improve CPR skills. Training sessions should utilise combinations of validated skill-specific training strategies, preferably including popular songs and feedback to help ensure skills retention. Refresher training, which focusses on skills and self-confidence rather than knowledge, should be undertaken every 3-6 months, although this timeframe needs further validation. All future studies assessing CPR psychomotor skills should adhere to a standardised reporting outcome list (proposed in this paper) to ensure consistency and comparability of results.

Pharmacometrics and systems pharmacology for metabolic bone diseases.

Mathematical modelling and simulation (M&S) of drug concentrations, pharmacologic effects and the (patho)physiologic systems within which they interact can be powerful tools for the preclinical, translational and clinical development of drugs. Indeed, the Prescription Drug User Fee Act (PDUFA VI), incorporated as part of the FDA Reauthorization Act of 2017 (FDARA), highlights the goal of advancing model-informed drug development (MIDD). MIDD can benefit development across many drug classes, including for metabolic bone diseases such as osteoporosis, cancer-related and numerous rare metabolic bone diseases; conditions characterized by significant morbidity and mortality. A drought looms in terms of the availability of new drugs to better treat these devastating diseases. This review provides an overview of several M&S approaches ranging from simple pharmacokinetic to integrated pharmacometric and systems pharmacology modelling. Examples are included to illustrate the use of these approaches during the development of several drugs for metabolic bone diseases such as bisphosphonates, denosumab, teriparatide and sclerostin inhibitors (romosozumab and blosozumab).

Perspective on the State of Pharmacometrics and Systems Pharmacology Integration.

Reliance on modeling and simulation in drug discovery and development has dramatically increased over the past decade. Two disciplines at the forefront of this activity, pharmacometrics and systems pharmacology (SP), emerged independently from different fields; consequently, a perception exists that only few examples integrate these approaches. Herein, we review the state of pharmacometrics and SP integration and describe benefits of combining these approaches in a model-informed drug discovery and development framework.