J Pharmacokinet Pharmacodyn [JOURNAL]
Sun
200 papers
Optimal sample selection applied to information rich, dense data.
Wang D, Hung T, Hung N … +3 more , Glue P, Jackson C, Duffull S
J Pharmacokinet Pharmacodyn · 2024 Feb · PMID 37561265 · Publisher ↗
Dense data can be classified into superdense information-poor data (type 1 dense data) and dense information-rich data (type 2 dense data). Arbitrary, random, or optimal thinning may be applied to type 1 dense data to mi...
Dense data can be classified into superdense information-poor data (type 1 dense data) and dense information-rich data (type 2 dense data). Arbitrary, random, or optimal thinning may be applied to type 1 dense data to minimise computational burden and statistical issues (such as autocorrelation). In contrast, a prospective or retrospective optimal design can be applied to type 2 dense data to maximise information gain from limited resources (capital and/or time). Here we describe a retrospective optimal selection strategy for quantification of unbound drug concentration from a discrete set of plasma samples where the total drug concentration has been measured.
Translational physiologically-based pharmacokinetic model for ocular disposition of monoclonal antibodies.
Naware S, Bussing D, Shah DK
J Pharmacokinet Pharmacodyn · 2024 Oct · PMID 37558929 · Publisher ↗
We have previously published a PBPK model comprising the ocular compartment to characterize the disposition of monoclonal antibodies (mAbs) in rabbits. While rabbits are commonly used preclinical species in ocular resear...
We have previously published a PBPK model comprising the ocular compartment to characterize the disposition of monoclonal antibodies (mAbs) in rabbits. While rabbits are commonly used preclinical species in ocular research, non-human primates (NHPs) have the most phylogenetic resemblance to humans including the presence of macula in the eyes as well as higher sequence homology. However, their use in ocular research is limited due to the strict ethical guidelines. Similarly, in humans the ocular samples cannot be collected except for the tapping of aqueous humor (AH). Therefore, we have translated this rabbit model to monkeys and human species using literature-reported datasets. Parameters describing the tissue volumes, physiological flows, and FcRn-binding were obtained from the literature, or estimated by fitting the model to the data. In the monkey model, the values for the rate of lysosomal degradation for antibodies (K), intraocular reflection coefficients (σ, σ σ), bidirectional rate of fluid circulation between the vitreous chamber and the aqueous chamber (Q), and permeability-surface area product of lens (PS) were estimated; and were found to be 31.5 h, 0.7629, 0.6982, 0.9999, 1.64 × 10 L/h, and 4.62 × 10 L/h, respectively. The monkey model could capture the data in plasma, aqueous humor, vitreous humor and retina reasonably well with the predictions being within twofold of the observed values. For the human model, only the value of K was estimated to fit the model to the plasma pharmacokinetics (PK) of mAbs and was found to be 24.4 h (4.14%). The human model could also capture the ocular PK data reasonably well with the predictions being within two- to threefold of observed values for the plasma, aqueous and vitreous humor. Thus, the proposed framework can be used to characterize and predict the PK of mAbs in the eye of monkey and human species following systemic and intravitreal administration. The model can also facilitate the development of new antibody-based therapeutics for the treatment of ocular diseases as well as predict ocular toxicities of such molecules following systemic administration.
Population pharmacokinetic modeling and dosing simulation of avalglucosidase alfa for selecting alternative dosing regimen in pediatric patients with late-onset pompe disease.
Tiraboschi G, Marchionni D, Tuffal G … +7 more , Fabre D, Martinez JM, Haack KA, Miossec P, Kittner B, Daba N, Hurbin F
J Pharmacokinet Pharmacodyn · 2023 Dec · PMID 37535240 · Full text
Avalglucosidase alfa (AVAL) was approved in the United States (2021) for patients with late-onset Pompe disease (LOPD), aged ≥ 1 year. In the present study, pharmacokinetic (PK) simulations were conducted to propose alte...
Avalglucosidase alfa (AVAL) was approved in the United States (2021) for patients with late-onset Pompe disease (LOPD), aged ≥ 1 year. In the present study, pharmacokinetic (PK) simulations were conducted to propose alternative dosing regimens for pediatric LOPD patients based on a bodyweight cut-off. Population PK (PopPK) analysis was performed using nonlinear mixed effect modeling approach on pooled data from three clinical trials with LOPD patients, and a phase 2 study (NCT03019406) with infantile-onset Pompe disease (IOPD: 1-12 years) patients. A total of 2257 concentration-time points from 91 patients (LOPD, n = 75; IOPD, n = 16) were included in the analysis. The model was bodyweight dependent allometric scaling with time varying bodyweight included on clearance and distribution volume. Simulations were performed for two dosing regimens (20 mg/kg or 40 mg/kg) with different bodyweight cut-off (25, 30, 35 and 40 kg) by generating virtual pediatric (1-17 years) and adult patients. Corresponding simulated individual exposures (maximal concentration, C and area under the curve in the 2-week dosing interval, AUC), and distributions were calculated. It was found that dosing of 40 mg/kg and 20 mg/kg in pediatric patients < 30 kg and ≥ 30 kg, respectively, achieved similar AVAL exposure (based on AUC) to adult patients receiving 20 mg/kg. PK simulations conducted on the basis of this model provided supporting data for the currently approved US labelling for dosing adapted bodyweight in LOPD patients ≥ 1 year by USFDA.
Computational neurosciences and quantitative systems pharmacology: a powerful combination for supporting drug development in neurodegenerative diseases.
Geerts H, Bergeler S, Lytton WW … +1 more , van der Graaf PH
J Pharmacokinet Pharmacodyn · 2024 Oct · PMID 37505397 · Publisher ↗
Successful clinical development of new therapeutic interventions is notoriously difficult, especially in neurodegenerative diseases, where predictive biomarkers are scarce and functional improvement is often based on pat...
Successful clinical development of new therapeutic interventions is notoriously difficult, especially in neurodegenerative diseases, where predictive biomarkers are scarce and functional improvement is often based on patient's perception, captured by structured interviews. As a consequence, mechanistic modeling of the processes relevant to therapeutic interventions in CNS disorders has been lagging behind other disease indications, probably because of the perceived complexity of the brain. However in this report, we develop the argument that a combination of Computational Neurosciences and Quantitative Systems Pharmacology (QSP) modeling of molecular pathways is a powerful simulation tool to enhance the probability of successful drug development for neurodegenerative diseases. Computational Neurosciences aims to predict action potential dynamics and neuronal circuit activation that are ultimately linked to behavioral changes and clinically relevant functional outcomes. These processes can not only be affected by the disease state, but also by common genotype variants on neurotransmitter-related proteins and the psycho-active medications often prescribed in these patient populations. Quantitative Systems Pharmacology (QSP) modeling of molecular pathways allows to simulate key pathological drivers of dementia, such as protein aggregation and neuroinflammatory responses. They often impact neurotransmitter homeostasis and voltage-gated ion-channels or lead to mitochondrial dysfunction, ultimately leading to changes in action potential dynamics and clinical readouts. Combining these two modeling approaches can lead to better actionable understanding of the many non-linear pharmacodynamic processes active in the human diseased brain. Practical applications include a rational selection of the optimal doses in combination therapies, identification of subjects more likely to respond to treatment, a more balanced stratification of treatment arms in terms of comedications, disease status and common genotype variants and re-analysis of small clinical trials to uncover a possible clinical signal. Ultimately this will lead to a higher success rate of bringing new therapeutics to the right patient populations.
Correction to: Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory.
White C, Rottschäfer V, Bridge L
J Pharmacokinet Pharmacodyn · 2024 Feb · PMID 37495832 · Full text
Abstract loading — click title to view on PubMed.
Go beyond the limits of genetic algorithm in daily covariate selection practice.
Ronchi D, Tosca EM, Bartolucci R … +1 more , Magni P
J Pharmacokinet Pharmacodyn · 2024 Apr · PMID 37493851 · Full text
Covariate identification is an important step in the development of a population pharmacokinetic/pharmacodynamic model. Among the different available approaches, the stepwise covariate model (SCM) is the most used. Howev...
Covariate identification is an important step in the development of a population pharmacokinetic/pharmacodynamic model. Among the different available approaches, the stepwise covariate model (SCM) is the most used. However, SCM is based on a local search strategy, in which the model-building process iteratively tests the addition or elimination of a single covariate at a time given all the others. This introduces a heuristic to limit the searching space and then the computational complexity, but, at the same time, can lead to a suboptimal solution. The application of genetic algorithms (GAs) for covariate selection has been proposed as a possible solution to overcome these limitations. However, their actual use during model building is limited by the extremely high computational costs and convergence issues, both related to the number of models being tested. In this paper, we proposed a new GA for covariate selection to address these challenges. The GA was first developed on a simulated case study where the heuristics introduced to overcome the limitations affecting currently available GA approaches resulted able to limit the selection of redundant covariates, increase replicability of results and reduce convergence times. Then, we tested the proposed GA on a real-world problem related to remifentanil. It obtained good results both in terms of selected covariates and fitness optimization, outperforming the SCM.
Generation and application of avatars in pharmacometric modelling.
Chasseloup E, Hooker AC, Karlsson MO
J Pharmacokinet Pharmacodyn · 2023 Oct · PMID 37488327 · Full text
Simulations from population models have critical applications in drug discovery and development. Avatars or digital twins, defined as individual simulations matching clinical criteria of interest compared to observations...
Simulations from population models have critical applications in drug discovery and development. Avatars or digital twins, defined as individual simulations matching clinical criteria of interest compared to observations from a real subject within a predefined margin of accuracy, may be a better option for simulations performed to inform future drug development stages in cases where an adequate model is not achievable. The aim of this work was to (1) investigate methods for generating avatars with pharmacometric models, and (2) explore the properties of the generated avatars to assess the impact of the different selection settings on the number of avatars per subject, their closeness to the individual observations, and the properties of the selected samples subset from the theoretical model parameters probability density function. Avatars were generated using different combinations of nature and number of clinical criteria, accuracy of agreement, and/or number of simulations for two examples models previously published (hemato-toxicity and integrated glucose-insulin model). The avatar distribution could be used to assess the appropriateness of the models assumed parameter distribution. Similarly it could be used to assess the models ability to properly describe the trajectories of the observations. Avatars can give nuanced information regarding the ability of a model to simulate data similar to the observations both at the population and at the individual level. Further potential applications for avatars may be as a diagnostic tool, an alternative to simulations with insurance to replicate key clinical features, and as an individual measure of model fit.
International society of Pharmacometrics Mentorship Program (IMP): feedback survey from the first cohort of mentor-mentee pairs.
Hanafin PO, Murthy A, Marathe D … +6 more , Diep JK, Krishnatry AS, Lon HK, Shah DK, Ait-Oudhia S, Rao GG
J Pharmacokinet Pharmacodyn · 2023 Aug · PMID 37480411 · Publisher ↗
The International Society of Pharmacometrics (ISoP) Mentorship Program (IMP) aims to help professionals at all career stages to transition into the pharmacometrics field, move to a different role/area within pharmacometr...
The International Society of Pharmacometrics (ISoP) Mentorship Program (IMP) aims to help professionals at all career stages to transition into the pharmacometrics field, move to a different role/area within pharmacometrics, or expand their skillsets. The program connects mentees at various stages of their careers with mentors based on established criteria for mentor-mentee matching. Pairing mentees with appropriate mentors ensures strong alignment between mentees' interests and mentors' expertise as this is critical to the success and continuation of the relationship between the mentor and mentee. Once mentors and mentees are connected, they are strongly encouraged to meet at least once per month for an hour. The mentor and mentee have the freedom to tailor their sessions to their liking, including frequency, duration, and topics they choose to focus on. Mentees are encouraged to clearly define their goals to help direct their mentor-mentee relationship and conversations. Mentees and mentors alike are given the opportunity to provide feedback about the program to the ISoP Education Committee through surveys and testimonials. Due to the program's infancy, structured guidelines for mentor-mentee sessions are still being developed and instituted using the program evaluation described in this paper.
A two-stages global sensitivity analysis by using the δ sensitivity index in presence of correlated inputs: application on a tumor growth inhibition model based on the dynamic energy budget theory.
De Carlo A, Tosca EM, Melillo N … +1 more , Magni P
J Pharmacokinet Pharmacodyn · 2023 Oct · PMID 37422844 · Full text
Global sensitivity analysis (GSA) evaluates the impact of variability and/or uncertainty of the model parameters on given model outputs. GSA is useful for assessing the quality of Pharmacometric model inference. Indeed,...
Global sensitivity analysis (GSA) evaluates the impact of variability and/or uncertainty of the model parameters on given model outputs. GSA is useful for assessing the quality of Pharmacometric model inference. Indeed, model parameters can be affected by high (estimation) uncertainty due to the sparsity of data. Independence between model parameters is a common assumption of GSA methods. However, ignoring (known) correlations between parameters may alter model predictions and, then, GSA results. To address this issue, a novel two-stages GSA technique based on the δ index, which is well-defined also in presence of correlated parameters, is here proposed. In the first step, statistical dependencies are neglected to identify parameters exerting causal effects. Correlations are introduced in the second step to consider the real distribution of the model output and investigate also the 'indirect' effects due to the correlation structure. The proposed two-stages GSA strategy was applied, as case study, to a preclinical tumor-in-host-growth inhibition model based on the Dynamic Energy Budget theory. The aim is to evaluate the impact of the model parameter estimate uncertainty (including correlations) on key model-derived metrics: the drug threshold concentration for tumor eradication, the tumor volume doubling time and a new index evaluating the drug efficacy-toxicity trade-off. This approach allowed to rank parameters according to their impact on the output, discerning whether a parameter mainly exerts a causal or 'indirect' effect. Thus, it was possible to identify uncertainties that should be necessarily reduced to obtain robust predictions for the outputs of interest.
Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory.
White C, Rottschäfer V, Bridge L
J Pharmacokinet Pharmacodyn · 2024 Feb · PMID 37389744 · Full text
Mathematical modelling has become a key tool in pharmacological analysis, towards understanding dynamics of cell signalling and quantifying ligand-receptor interactions. Ordinary differential equation (ODE) models in rec...
Mathematical modelling has become a key tool in pharmacological analysis, towards understanding dynamics of cell signalling and quantifying ligand-receptor interactions. Ordinary differential equation (ODE) models in receptor theory may be used to parameterise such interactions using timecourse data, but attention needs to be paid to the theoretical identifiability of the parameters of interest. Identifiability analysis is an often overlooked step in many bio-modelling works. In this paper we introduce structural identifiability analysis (SIA) to the field of receptor theory by applying three classical SIA methods (transfer function, Taylor Series and similarity transformation) to ligand-receptor binding models of biological importance (single ligand and Motulsky-Mahan competition binding at monomers, and a recently presented model of a single ligand binding at receptor dimers). New results are obtained which indicate the identifiable parameters for a single timecourse for Motulsky-Mahan binding and dimerised receptor binding. Importantly, we further consider combinations of experiments which may be performed to overcome issues of non-identifiability, to ensure the practical applicability of the work. The three SIA methods are demonstrated through a tutorial-style approach, using detailed calculations, which show the methods to be tractable for the low-dimensional ODE models.
Assessing the performance of QSP models: biology as the driver for validation.
Singh FA, Afzal N, Smithline SJ … +1 more , Thalhauser CJ
J Pharmacokinet Pharmacodyn · 2024 Oct · PMID 37386340 · Full text
Validation of a quantitative model is a critical step in establishing confidence in the model's suitability for whatever analysis it was designed. While processes for validation are well-established in the statistical sc...
Validation of a quantitative model is a critical step in establishing confidence in the model's suitability for whatever analysis it was designed. While processes for validation are well-established in the statistical sciences, the field of quantitative systems pharmacology (QSP) has taken a more piecemeal approach to defining and demonstrating validation. Although classical statistical methods can be used in a QSP context, proper validation of a mechanistic systems model requires a more nuanced approach to what precisely is being validated, and what role said validation plays in the larger context of the analysis. In this review, we summarize current thoughts of QSP validation in the scientific community, contrast the aims of statistical validation from several contexts (including inference, pharmacometrics analysis, and machine learning) with the challenges faced in QSP analysis, and use examples from published QSP models to define different stages or levels of validation, any of which may be sufficient depending on the context at hand.
Clinical validation of translational antibody PBPK model using tissue distribution data generated with Zr-immuno-PET imaging.
Liu S, Li Z, Huisman M … +1 more , Shah DK
J Pharmacokinet Pharmacodyn · 2023 Oct · PMID 37382712 · Publisher ↗
The main objective of this manuscript was to validate the ability of the monoclonal antibody physiologically-based pharmacokinetic (PBPK) model to predict tissue concentrations of antibodies in the human. To accomplish t...
The main objective of this manuscript was to validate the ability of the monoclonal antibody physiologically-based pharmacokinetic (PBPK) model to predict tissue concentrations of antibodies in the human. To accomplish this goal, preclinical and clinical tissue distribution and positron emission tomography imaging data generated using zirconium-89 (Zr) labeled antibodies were obtained from the literature. First, our previously published translational PBPK model for antibodies was expanded to describe the whole-body biodistribution of Zr labeled antibody and the free Zr, as well as residualization of free Zr. Subsequently, the model was optimized using mouse biodistribution data, where it was observed that free Zr mainly residualizes in the bone and the extent of antibody distribution in certain tissues (e.g., liver and spleen) may be altered by labeling with Zr. The mouse PBPK model was scaled to rat, monkey, and human by simply changing the physiological parameters, and a priori simulations performed by the model were compared with the observed PK data. It was found that model predicted antibody PK in majority of the tissues in all the species superimposed over the observed data, and the model was also able to predict the PK of antibody in human tissues reasonably well. As such, the work presented here provides unprecedented evaluation of the antibody PPBK model for its ability to predict tissue PK of antibodies in the clinic. This model can be used for preclinical-to-clinical translation of antibodies and for prediction of antibody concentrations at the site-of-action in the clinic.
Physiologically-based pharmacokinetic modeling to predict drug-drug interaction of enzalutamide with combined P-gp and CYP3A substrates.
Otsuka Y, Poondru S, Bonate PL … +4 more , Rose RH, Jamei M, Ushigome F, Minematsu T
J Pharmacokinet Pharmacodyn · 2023 Oct · PMID 37344637 · Full text
Enzalutamide is known to strongly induce cytochrome P450 3A4 (CYP3A4). Furthermore, enzalutamide showed induction and inhibition of P-glycoprotein (P-gp) in in vitro studies. A clinical drug-drug interaction (DDI) study...
Enzalutamide is known to strongly induce cytochrome P450 3A4 (CYP3A4). Furthermore, enzalutamide showed induction and inhibition of P-glycoprotein (P-gp) in in vitro studies. A clinical drug-drug interaction (DDI) study between enzalutamide and digoxin, a typical P-gp substrate, suggested enzalutamide has weak inhibitory effect on P-gp substrates. Direct oral anticoagulants (DOACs), such as apixaban and rivaroxaban, are dual substrates of CYP3A4 and P-gp, and hence it is recommended to avoid co-administration of these DOACs with combined P-gp and strong CYP3A inducers. Enzalutamide's net effect on P-gp and CYP3A for apixaban and rivaroxaban plasma exposures is of interest to physicians who treat patients for venous thromboembolism with prostate cancer. Accordingly, a physiologically-based pharmacokinetic (PBPK) analysis was performed to predict the magnitude of DDI on apixaban and rivaroxaban exposures in the presence of 160 mg once-daily dosing of enzalutamide. The PBPK models of enzalutamide and M2, a major metabolite of enzalutamide which also has potential to induce CYP3A and P-gp and inhibit P-gp, were developed and verified as perpetrators of CYP3A-and P-gp-mediated interaction. Simulation results predicted a 31% decrease in AUC and no change in C for apixaban and a 45% decrease in AUC and a 25% decrease in C for rivaroxaban when 160 mg multiple doses of enzalutamide were co-administered. In summary, enzalutamide is considered to decrease apixaban and rivaroxaban exposure through the combined effects of CYP3A induction and net P-gp inhibition. Concurrent use of these drugs warrants careful monitoring for efficacy and safety.
Challenges, approaches and enablers: effectively triangulating towards dose selection in pediatric rare diseases.
Durairaj C, Bhattacharya I
J Pharmacokinet Pharmacodyn · 2023 Dec · PMID 37296230 · Publisher ↗
Dose selection is an integral part of a molecule's journey to become medicine. On top of typical challenges faced in dose selection for more common diseases, pediatric rare disease has additional unique challenges due to...
Dose selection is an integral part of a molecule's journey to become medicine. On top of typical challenges faced in dose selection for more common diseases, pediatric rare disease has additional unique challenges due to the combination of 'rare' and 'pediatric' populations. Using the central theme of maximizing 'relevant' information to overcome information paucity, dose selection strategy in pediatric rare diseases is discussed using a triangulation concept involving challenges, approaches and very importantly, enablers. Using actual examples, unique scenarios are discussed where specific enablers allowed certain approaches to be used to overcome the challenges. The continued need for model-informed drug development is also discussed using examples of where modeling and simulation tools have been successfully used in bridging available information to select pediatric doses in rare disease. Additionally, challenges with translation and associated dose selection of new modalities such as gene therapy in rare diseases are examined with the lens of continuous learning and knowledge development that will enable pediatric dose selection of these modalities with confidence.
Population pharmacokinetic and pharmacokinetic-pharmacodynamic modeling of bempedoic acid and low-density lipoprotein cholesterol in healthy subjects and patients with dyslipidemia.
Jadhav SB, Amore BM, Bockbrader H … +4 more , Crass RL, Chapel S, Sasiela WJ, Emery MG
J Pharmacokinet Pharmacodyn · 2023 Oct · PMID 37243877 · Full text
Population pharmacokinetics (popPK) of bempedoic acid and the popPK/pharmacodynamic (popPK/PD) relationship between bempedoic acid concentrations and serum low-density lipoprotein cholesterol (LDL-C) from baseline were c...
Population pharmacokinetics (popPK) of bempedoic acid and the popPK/pharmacodynamic (popPK/PD) relationship between bempedoic acid concentrations and serum low-density lipoprotein cholesterol (LDL-C) from baseline were characterized. A two-compartment disposition model with a transit absorption compartment and linear elimination best described bempedoic acid oral pharmacokinetics (PK). Multiple covariates, including renal function, sex, and weight, had statistically significant effects on the predicted steady-state area under the curve. Mild (estimated glomerular filtration rate (eGFR) 60 to < 90 mL/min vs. ≥ 90 mL/min) and moderate (eGFR 30 to < 60 mL/min vs. ≥ 90 mL/min) renal impairment, female sex, low (< 70 kg vs. 70-100 kg) and high (> 100 kg vs. 70-100 kg) body weight were predicted to have a 1.36-fold (90% confidence interval (CI) 1.32, 1.41), 1.85-fold (90% CI 1.74, 2.00), 1.39-fold (90% CI 1.34, 1.47), 1.35-fold (90% CI 1.30, 1.41), and 0.75-fold (90% CI 0.72, 0.79) exposure difference relative to their reference populations, respectively. An indirect response model described changes in serum LDL-C with a model-predicted 35% maximal reduction and bempedoic acid IC of 3.17 µg/mL. A 28% reduction from LDL-C baseline was predicted for a steady-state average concentration of 12.5 µg/mL after bempedoic acid (180 mg/day) dosing, accounting for approximately 80% of the predicted maximal LDL-C reduction. Concurrent statin therapy, regardless of intensity, reduced the maximal effect of bempedoic acid but resulted in similar steady-state LDL-C levels. While multiple covariates had statistically significant effects on PK and LDL-C lowering, none were predicted to warrant bempedoic acid dose adjustment.
Empirical bayes approach for dynamic bayesian borrowing for clinical trials in rare diseases.
Sebastien B
J Pharmacokinet Pharmacodyn · 2023 Dec · PMID 37148459 · Publisher ↗
Application of Bayesian methods is one the tools that can be used to face the multiple challenges that are met when clinical trials must be conducted in rare diseases. We propose in this work to use a dynamic Bayesian bo...
Application of Bayesian methods is one the tools that can be used to face the multiple challenges that are met when clinical trials must be conducted in rare diseases. We propose in this work to use a dynamic Bayesian borrowing approach, based on a mixture prior, to complement the control arm of a comparative trial and estimate the mixture parameter by an Empirical Bayes approach. The method is compared, using simulations, with an approach based on a pre-specified (non-adaptive) informative prior. The simulation study shows that the proposed method exhibits similar power as the non-adaptive prior and drastically reduce type I error in case of severe discrepancy between the informative prior and the study control arm data. In case of limited discrepancy between the informative prior and the study control arm data, then our proposed adaptive prior does not reduce the inflation of the type I error.
Achieving big with small: quantitative clinical pharmacology tools for drug development in pediatric rare diseases.
Ahmed MA, Burnham J, Dwivedi G … +1 more , AbuAsal B
J Pharmacokinet Pharmacodyn · 2023 Dec · PMID 37140724 · Publisher ↗
Pediatric populations represent a major fraction of rare diseases and compound the intrinsic challenges of pediatric drug development and drug development for rare diseases. The intertwined complexities of pediatric and...
Pediatric populations represent a major fraction of rare diseases and compound the intrinsic challenges of pediatric drug development and drug development for rare diseases. The intertwined complexities of pediatric and rare disease populations impose unique challenges to clinical pharmacologists and require integration of novel clinical pharmacology and quantitative tools to overcome multiple hurdles during the discovery and development of new therapies. Drug development strategies for pediatric rare diseases continue to evolve to meet the inherent challenges and produce new medicines. Advances in quantitative clinical pharmacology research have been a key component in advancing pediatric rare disease research to accelerate drug development and inform regulatory decisions. This article will discuss the evolution of the regulatory landscape in pediatric rare diseases, the challenges encountered during the design of rare disease drug development programs and will highlight the use of innovative tools and potential solutions for future development programs.
The future of rare disease drug development: the rare disease cures accelerator data analytics platform (RDCA-DAP).
Barrett JS, Betourne A, Walls RL … +5 more , Lasater K, Russell S, Borens A, Rohatagi S, Roddy W
J Pharmacokinet Pharmacodyn · 2023 Dec · PMID 37131052 · Full text
Rare disease drug development is wrought with challenges not the least of which is access to the limited data currently available throughout the rare disease ecosystem where sharing of the available data is not guarantee...
Rare disease drug development is wrought with challenges not the least of which is access to the limited data currently available throughout the rare disease ecosystem where sharing of the available data is not guaranteed. Most pharmaceutical sponsors seeking to develop agents to treat rare diseases will initiate data landscaping efforts to identify various data sources that might be informative with respect to disease prevalence, patient selection and identification, disease progression and any data projecting likelihood of patient response to therapy including any genetic data. Such data are often difficult to come by for highly prevalent, mainstream disease populations let alone for the 8000 rare disease that make up the pooled patient population of rare disease patients. The future of rare disease drug development will hopefully rely on increased data sharing and collaboration among the entire rare disease ecosystem. One path to achieving this outcome has been the development of the rare disease cures accelerator, data analytics platform (RDCA-DAP) funded by the US FDA and operationalized by the Critical Path Institute. FDA intentions were clearly focused on improving the quality of rare disease regulatory applications by sponsors seeking to develop treatment options for various rare disease populations. As this initiative moves into its second year of operations it is envisioned that the increased connectivity to new and diverse data streams and tools will result in solutions that benefit the entire rare disease ecosystem and that the platform becomes a Collaboratory for engagement of this ecosystem that also includes patients and caregivers.
An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi- or fully-mechanistic models and exact steady state solutions.
Guzzetti S, Morentin Gutierrez P
J Pharmacokinet Pharmacodyn · 2023 Oct · PMID 37120680 · Full text
The value of an integrated mathematical modelling approach for protein degraders which combines the benefits of traditional turnover models and fully mechanistic models is presented. Firstly, we show how exact solutions...
The value of an integrated mathematical modelling approach for protein degraders which combines the benefits of traditional turnover models and fully mechanistic models is presented. Firstly, we show how exact solutions of the mechanistic models of monovalent and bivalent degraders can provide insight on the role of each system parameter in driving the pharmacological response. We show how on/off binding rates and degradation rates are related to potency and maximal effect of monovalent degraders, and how such relationship can be used to suggest a compound optimization strategy. Even convoluted exact steady state solutions for bivalent degraders provide insight on the type of observations required to ensure the predictive capacity of a mechanistic approach. Specifically for PROTACs, the structure of the exact steady state solution suggests that the total remaining target at steady state, which is easily accessible experimentally, is insufficient to reconstruct the state of the whole system at equilibrium and observations on different species (such as binary/ternary complexes) are necessary. Secondly, global sensitivity analysis of fully mechanistic models for PROTACs suggests that both target and ligase baselines (actually, their ratio) are the major sources of variability in the response of non-cooperative systems, which speaks to the importance of characterizing their distribution in the target patient population. Finally, we propose a pragmatic modelling approach which incorporates the insights generated with fully mechanistic models into simpler turnover models to improve their predictive ability, hence enabling acceleration of drug discovery programs and increased probability of success in the clinic.
Special issue: Model-informed drug development in rare diseases: connecting the dots in an information rich ecosystem.
Krishna R
J Pharmacokinet Pharmacodyn · 2023 Dec · PMID 37103676 · Publisher ↗
Abstract loading — click title to view on PubMed.
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