Why has model-informed precision dosing not yet become common clinical reality? lessons from the past and a roadmap for the future.

Patient groups prone to polypharmacy and special subpopulations are susceptible to suboptimal treatment. Refined dosing in special populations is imperative to improve therapeutic response and/or lowering the risk of toxicity. Model-informed precision dosing (MIPD) may improve treatment outcomes by achieving the optimal dose for an individual patient. There is, however, relatively little published evidence of large-scale utility and impact of MIPD, where it is often implemented as local collaborative efforts between academia and healthcare. This article highlights some successful applications of bringing MIPD to clinical care and proposes strategies for wider integration in healthcare. Considerations are brought up herein that will need addressing to see MIPD become "widespread clinical practice," among those, wider interdisciplinary collaborations and the necessity for further evidence-based efficacy and cost-benefit analysis of MIPD in healthcare. The implications of MIPD on regulatory policies and pharmaceutical development are also discussed as part of the roadmap.

Multiscale modeling in drug discovery and development: future opportunities and present challenges.

Modeling and simulation (M&S) is a diverse discipline that uses tools from mathematics, statistics, and computer science to arrive at quantitative predictions. Its application to biological systems has a checkered history with respect to success and failure. The instances of failure may reflect the heterogeneity of M&S approaches. In order to ensure a successful outcome, M&S must be fit for purpose and responsive; connecting with biological concepts is critical; sharing of results has to be done properly; buy-in is achieved in stages. These concepts are being applied in drug discovery and development and are yielding success. However, bottlenecks remain.

Are we optimizing gestational diabetes treatment with glyburide? The pharmacologic basis for better clinical practice.

Glyburide's pharmacokinetics (PK) and pharmacodynamics have not been studied in women with gestational diabetes mellitus (GDM). The objective of this study was to assess steady-state PK of glyburide, as well as insulin sensitivity, beta-cell responsivity, and overall disposition indices after a mixed-meal tolerance test (MMTT) in women with GDM (n = 40), nonpregnant women with type 2 diabetes mellitus (T2DM) (n = 26), and healthy pregnant women (n = 40, MMTT only). At equivalent doses, glyburide plasma concentrations were approximately 50% lower in pregnant women than in nonpregnant subjects. The average umbilical cord/maternal plasma glyburide concentration ratio at the time of delivery was 0.7 +/- 0.4. Insulin sensitivity was approximately fivefold lower in women with GDM as compared with healthy pregnant women. Despite comparable beta-cell responsivity indices, the average beta-cell function corrected for insulin resistance was more than 3.5-fold lower in women with glyburide-treated GDM than in healthy pregnant women. Women with GDM in whom glyburide treatment has failed may benefit from alternative medication or dosage escalation; however, fetal safety should be kept in mind.

Amoxicillin pharmacokinetics in pregnant women: modeling and simulations of dosage strategies.

Amoxicillin is recommended for anthrax prevention in pregnancy. The objective of this study was to evaluate the pharmacokinetics of amoxicillin during pregnancy and postpartum (PP). Sixteen women received amoxicillin during gestation (18-22 weeks (T2) and 30-34 weeks (T3)) as well as 3 months postpartum (PP) to evaluate single-dose pharmacokinetics. Amoxicillin compartmental pharmacokinetic parameters were used to simulate amoxicillin concentration-time profiles following different dosage strategies. Amoxicillin CL(renal) (T2: 24.8+/-6.7 l/h, P<0.001; T3: 24.0+/-3.9 l/h, P<0.001; and PP: 15.3+/-2.6 l/h) and renal CL(secretion) (T2: 280+/-105 ml/min, P<0.002; T3: 259+/-54 ml/min, P<0.001; and PP: 167+/-47 ml/min) were higher during pregnancy than postpartum. Simulations suggest that amoxicillin concentrations adequate to prevent anthrax may be difficult to achieve during pregnancy and postpartum. Increases in amoxicillin CL(renal) and renal CL(secretion) reflect increases in filtration and secretory transport or diminished reabsorption in the kidneys. Amoxicillin may not be an appropriate antibiotic for post-anthrax exposure prophylaxis.

2-Amino-Benzo[d]isothiazol-3-one derivatives: synthesis and assessment of their antiplatelet/spasmolytic effects.

We describe a series of 2-amino-benzo[d]isothiazol-3-one derivatives (2-8), which were synthesized and screened in vitro for inhibition of platelet aggregation and for their spasmolytic activity, with the awareness that the development of antiplatelet agents with additional vasodilation activity could be beneficial in the treatment of various vaso-occlusive disorders. The tested compounds show a powerful antiplatelet activity and various modifications resulted in molecules possessing antiaggregating effects as well as spasmolytic actions.

Identifiability and interval identifiability of mammillary and catenary compartmental models with some known rate constants.

The identifiability problem is addressed for n-compartment linear mammillary and catenary models, for the common case of input and output in the first compartment and prior information about one or more model rate constants. We first define the concept of independent constraints and show that n-compartment linear mammillary or catenary models are uniquely identifiable under n-1 independent constraints. Closed-form algorithms for bounding the constrained parameter space are then developed algebraically, and their validity is confirmed using an independent approach, namely joint estimation of the parameters of all uniquely identifiable submodels of the original multicompartmental model. For the noise-free (deterministic) case, the major effects of additional parameter knowledge are to narrow the bounds of rate constants that remain unidentifiable, as well as to possibly render others identifiable. When noisy data are considered, the means of the bounds of rate constants that remain unidentifiable are also narrowed, but the variances of some of these bound estimates increase. This unexpected result was verified by Monte Carlo simulation of several different models, using both normally and lognormally distributed data assumptions. Extensions and some consequences of this analysis useful for model discrimination and experiment design applications are also noted.

Glucose effectiveness and insulin sensitivity from the minimal models: consequences of undermodeling assessed by Monte Carlo simulation.

The unlabeled (cold) minimal model (MM) and the labeled (hot) minimal model (HMM) are a powerful tool to investigate in vivo metabolism from a standard intravenous glucose tolerance test (IVGTT) or hot IVGTT (HIVGTT). They allow to estimate metabolic indexes of the glucose-insulin system, namely glucose effectiveness (GE) and insulin sensitivity (IS) (of uptake and production those of MM, and of uptake only those of HMM). Here, the consequences of the single-compartment glucose kinetics approximation used in the MM's are investigated via Monte Carlo simulation, using a physiologic reference model (RM) of the system. RM allows to generate noisy synthetic plasma concentrations of glucose, tracer glucose, and insulin during IVGTT and HIVGTT, which are then analyzed with MM and HMM. The MM and HMM GE and IS are then compared with the RM ones. Results of 400 runs show that: 1) correlation of MM GE with the RM index is weak; 2) MM IS is well correlated with the RM index, but severely underestimates it; 3) HMM clearance rate is correlated with RM clearance; and 4) HMM IS is well correlated and only slightly overestimates the RM index. These results demonstrate that GE of MM is most affected by the single-compartment approximation and the indexes of HMM are more robust than those of MM.

Parameter estimation in distributed models of blood-tissue exchange: a Monte Carlo strategy to assess precision.

Distributed parameter models of blood-tissue exchange are increasingly used to interpret multiple tracer dilution data in regional kinetic studies. To derive a measure of the precision with which the model parameters are estimated is therefore of paramount importance. The standard approach to deriving precision of estimates does not take into account the fact that some of the model parameters are fixed. Thus, the precision of parameter estimates is not realistic and, in all likelihood, it is overestimated. The aim of this study is to describe a Monte Carlo method devised to obtain a theoretically sound measure of the precision of estimates, which takes into account both measurement error and the uncertainty associated with the fixed parameters. The fixed parameter values are taken from a probability distribution. By letting the fixed parameters vary according to their distribution, a large number of synthetic datasets is generated. Noise is then added. Estimating the parameters in each of these synthetic datasets allows the derivation of a Monte Carlo mean and standard deviation, which provides a realistic measure of precision. The methodology is illustrated for a simulated data case study dealing with the estimation of the capillary permeability-surface area product in a two tracer experiment.

The hot but not the cold minimal model allows precise assessment of insulin sensitivity in NIDDM subjects.

Assessment of insulin sensitivity in subjects with non-insulin-dependent diabetes mellitus (NIDDM) is of paramount importance but intrinsically difficult. The standard (hereafter cold) minimal model, in conjunction with an insulin-modified protocol, has been recently proposed, but the estimates of insulin sensitivity showed poor precision (Saad et al. Diabetes 43: 1114-1121, 1994). We propose the tracer (hereafter hot) minimal model as a highly reliable method to estimate insulin sensitivity (SI*) and fractional glucose clearance (SG*), reflecting glucose disposal only, in NIDDM subjects. A [6,6- 2H2] glucose-labeled insulin-modified intravenous glucose tolerance test was performed in seven NIDDM subjects. In particular, SI* was 1.07 +/- 0.34 x10(-4)min(-1).microU-1.ml estimated with an average precision (mean coefficient of variation of 12%, range 4-22%), whereas the cold minimal model SI was 0.96 +/- 0.26 x 10(-4) min-1. microU-1.ml (mean coefficient of variation of 105%, range 3-353%). Another advantage of the hot indexes with respect to the cold indexes is their ability to reflect glucose and insulin effect on glucose disposal only, and not also on hepatic glucose production. Finally, we also studied by simulation the effect of glucose urinary loss on cold and hot minimal model indexes; only cold glucose effectiveness (SG) was significantly affected, resulting in a mean approximately 40% lower. The hot minimal model appears therefore more reliable than the cold model for assessing glucose tolerance in NIDDM subjects. In particular its ability to dissect disposal from production processes, coupled with the very good precision of the estimated metabolic indexes, supports the clinical use of this method in NIDDM subjects.