- Top of page
- What is Already Known about This Subject
- What This Study Adds
- Competing Interests
- Appendix: Appendix 1
Imatinib is a selective inhibitor of tyrosine kinases, comprising Bcr-Abl fusion protein in chronic myeloid leukaemia (CML) and the c-kit proto-oncogene in gastrointestinal stromal tumour (GIST). It has demonstrated an impressive clinical efficacy in both malignancies. Inducing durable responses and achieving prolonged survival, imatinib has become the standard of care for the treatment of these diseases. However, imatinib treatment is not devoid of toxicity and resistance occurs in some instances. Besides cellular mechanisms of resistance (gene amplification and mutation), variability in binding to α1-acid glycoprotein (AGP) can modulate its activity [1-5]. Indeed, only the free drug is likely to equilibrate with the intracellular milieu to exert its pharmacological action. Moreover, a small change in the extent of protein binding may result in a significant impact on imatinib free fraction and on its concentration–effect relationships [5, 6].
Although there are many circulating proteins in plasma capable of binding drugs, the majority of drugs bind to human serum albumin (HSA) and AGP . HSA is the most abundant protein in plasma, whereas the normal AGP concentrations are much lower, resulting in a lower capacity to bind drugs and a more rapid saturation of the protein . Both are capable of binding a broad variety of drugs with sufficient affinity to impact on the pharmacologically active free fraction. HSA is the primary binding protein for acidic drugs, while binding to AGP is more commonly observed with basic lipophilic agents. Changes in the concentration, conformation, and/or other physicochemical characteristics of these proteins may result in significant changes in the drug free fraction . Alterations in albumin concentrations in plasma occur as a result of altered synthesis, loss, or a shift of fluids between body compartments. The most common alteration, hypoalbuminaemia, is associated with a wide variety of pathologic and physiologic conditions, such as inflammation, nephrotic syndrome, burn injury and cancer. Similarly, several disease states (inflammation, renal and hepatic disease), physiological conditions (age, pregnancy, obesity), genetic factors or co-administration of some drugs can markedly alter AGP concentrations (see  for review).
Binding studies of imatinib in plasma, in particular to HSA and AGP, showed that imatinib binds mainly to AGP, resulting in a mean free fraction of about 4% [4, 10]. An in vitro binding study performed by Kretz et al. , with radioactive imatinib incubated in conditions reflecting the clinical situation, reported free fractions of 3.1% and 20% in AGP and HSA solutions, respectively, and of 4.3% in healthy human plasma. Several studies showed that imatinib has about 50 to 60-fold higher affinity for AGP than for HSA [2, 11]. AGP of most individuals is a mixture of two or three genetic variants (F1 and/or S and A), which are encoded by two different genes [12, 13]. The F1 and S are encoded by alleles of the same gene, while the A variant is the product of the other gene [12, 13]. Moreover, it has been shown that imatinib binds with a stronger selectivity to the F1-S genetic variant of AGP, while its binding on the A variant is weaker and less specific [12, 13].
For drugs of intermediate to low hepatic extraction and high protein binding such as imatinib, a change in protein concentrations or in binding affinity alters total plasma concentrations, while free drug concentrations are expected to remain mostly unchanged . However, an altered free fraction modifies the apparent total concentration−effect relationships, and may thus compromise the correct interpretation of therapeutic drug monitoring results based on total plasma concentrations, especially for strongly protein bound drugs such as imatinib. Moreover, under certain pathophysiological conditions competing with normal binding, such as uraemia, liver disease, hypoproteinaemia or drug interactions, free drug concentration may be significantly elevated despite total concentrations within the therapeutic range .
The relationship between unbound drug exposure and efficacy and toxicity endpoints has been recently investigated. Studies have shown that unbound drug exposure was correlated with haematological toxicity (absolute neutrophil count), whereas no significant association with treatment efficacy in GIST patients could be detected [16, 17]. Widmer et al.  confirmed that both total (in GIST) and free imatinib exposure (in CML and GIST) were correlated with the occurrence and number of side effects, and that higher free drug exposure also predicted a higher probability of therapeutic response in GIST when taking into account tumour KIT genotype . All these results were however obtained using extrapolated free concentrations rather than real measurements. A formal confirmation of them is therefore still warranted.
The aim of the present study was to extend our previously proposed model enabling the prediction of free imatinib concentrations based on total imatinib concentrations . The objectives were therefore (i) to characterize the population pharmacokinetics of total and free imatinib plasma concentrations, (ii) to evaluate the influence of both AGP and HSA concentrations in addition to demographic variables and co-medications on total and free imatinib pharmacokinetics and (iii) to refine our model for the prediction of imatinib free concentrations based on total concentrations along with other potential influencing factors.
- Top of page
- What is Already Known about This Subject
- What This Study Adds
- Competing Interests
- Appendix: Appendix 1
This study allowed for the first time the full characterization of the pharmacokinetic profile of total and unbound imatinib concentrations and the description of the relationships governing the equilibrium between total and free imatinib concentrations.
The population pharmacokinetics of total imatinib concentrations could be adequately described using a one compartment model for total and unbound concentrations. The estimated values of CLtot and Vd,tot are in close accordance with our previous results and in good agreement with previously reported studies [10, 30-32]. Intersubject variabilities on CL and Vd were of the same magnitude for total and free concentrations, although poorly estimated. Among the tested covariates, only AGP had a significant influence on CLtot. It is expected that an increase in AGP concentrations induces a reduction in the free fraction of imatinib, therefore decreasing total clearance, whereas CLu remains unchanged. The resulting effect is an increase in total plasma concentrations despite constant unbound concentrations. No effect of HSA concentrations was observed in univariate analyses. The interaction model revealed a small influence of this protein, which could indicate that HSA, a carrier with lower affinity but higher capacity, might have a small residual influence once accounting for the predominant effect of AGP. The prediction of Cu was, however, not improved using this more complicated model. In addition, owing to the much larger amount of HSA in blood and its lower affinity to imatinib, it is not expected that changes in HSA concentrations could alter imatinib concentrations to a significant extent at therapeutic concentrations. Furthermore, although the model including non-linear binding to both AGP and HSA could not be tested due to the complexity of the relationship, it is very unlikely that saturation of HSA occurs at therapeutic concentrations.
Among the demographic covariates tested, only body weight was associated with a small increase in CLtot and CLu, and Vd,tot and Vd,u in our population, which did not reach statistical significance. Menon-Andersen et al. showed that total body weight was the only covariate found to affect CLtot and Vd,tot and reported an increased clearance by 23% and Vd by 32% on body weight doubling . The study of Schmidli et al.  revealed a small and similar 12% increase in CLtot on doubling body weight and a 32% increase in Vd,tot. A much more important effect of body weight was found in our previous study , which increased CLtot and CLu by 99% and 91%, respectively. The lack of correlation might be related to power issues, the range of body weight being relatively restricted in our population (SD ± 15). Gender and age were not shown to affect imatinib pharmacokinetics, in accordance with several studies having reported that both factors are unlikely to be clinically significant in GIST and CML patients [10, 30, 33-35]. Whether these demographic parameters have an influence on imatinib free concentrations or not should be confirmed. No influence of comedications was found, owing probably to the limited number of patients with cytochromes P450 3A4 inducers or inhibitors. No effect of proton pump inhibitors was found either . Previous studies with Mg2+/Al3+-based antacids had shown negative results as well .
We could challenge our previous theoretical model  using experimental measurements of unbound imatinib concentrations actually determined in GIST patients. The average imatinib free fraction estimated in our study (3.5%) is in close agreement with free fractions reported of 4% , 3.1%  and 5%  in the literature. HSA and AGP are the most important drug-binding proteins in plasma. In healthy subjects, the concentration of AGP in plasma varies in the range of 0.55–1.4 g l−1 . HSA, the major protein component of plasma , is present in the plasma of healthy individuals at concentrations ranging from 35–52 g l−1 . The modular structural organization of HSA provides a variety of ligand binding sites, although two appear to predominate .
AGP plasma concentrations proved to have a marked influence on imatinib bound pharmacokinetics, whereas HSA did not show any relevant effect. The reported association binding constant (Ka) values of imatinib to AGP are of 4.9 × 106 m−1, 2.4 × 106 m−1, 1.7 × 106 m−1 and 1.4 × 106 m−1 [2, 3, 11], corresponding to Kd values of respectively 100, 210, 290 and 350 ng ml−1. These values are in good agreement with the Kd of 319 ng ml−1 estimated in this study, using non-linear binding and assuming a 1:1 binding ratio (4). Ka values of 2.3 × 105 m−1, 3 × 104 m−1 and 7 × 103 m−1 [2-4, 11], have been reported for imatinib binding to HSA, suggesting very different Kd values (2100, 16 500 and 70 500 ng ml−1) and a much weaker affinity for HSA. These higher values are in accordance with the estimated Kd for albumin in our study. The limited additional effect of HSA on imatinib kinetics observed in our study is thus compatible with its approximately 60 times higher affinity for AGP than for HSA . The major bias observed by applying our previous model was solely due to the inaccurate estimation of the Kd value determined without actual free imatinib concentrations measurements, which underestimated unbound concentrations by a factor of three approximately. It must be recalled that this study used a fairly indirect method to estimate Kd only from total concentration values . The small residual and non-significant bias might be related to a few data points measured during the absorption phase that were under-estimated by the model.
Despite the validity of this approach in our population, it must be acknowledged that the prediction of Cu had some limitations. Indeed, some pathophysiological conditions or concomitant medications could alter the binding affinity or capacity to AGP, resulting in changes in unbound concentrations that are not directly proportional to changes in protein levels. Confounding factors have thus to be considered while using this approach for the prediction of unbound concentrations.
In conclusion, the prediction of free imatinib concentrations can be based on measurement of total concentrations and AGP concentrations using the following relationship:
where Cu and Ctot are expressed in ng ml−1 and AGPtot in g l−1.
Individualization of imatinib therapy based on free imatinib plasma concentration extrapolated with our model could be advised, in particular when changes in plasma binding are expected under specific pathophysiological conditions. Either the determination of free concentrations of imatinib or its model-derived estimation in a larger population of patients might also help to understand better the relationship between free concentrations and efficacy or toxicity . In routine TDM practice, this approach could represent a more practical and affordable method to derive free concentrations of imatinib from total concentrations, having only to take into account AGP plasma concentration measurements.