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2 Evaluation of Drug Exposure-Antitumor Effect Relationships

2 Evaluation of Drug Exposure-Antitumor Effect Relationships

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A. Ruiz-Garcia and K. Yamazaki

two types of transduction models, the signal transduction model and the cell

distribution model. Transduction models consist of homogenous multiple transit

compartments, where it is assumed that the pharmacokinetics of the anticancer

agent does not affect the time of signal propagation. The multiple transit compartments have a mean transit time (MTR) that accounts for a time delay in the pharmacological response relative to systemic drug exposure. These transduction models

are often called semi-mechanistic, because they bring increased realism compared

to the indirect response models. Transduction models have been mainly applied to

date to characterize the PKPD relationships of cytotoxic agents (Simeoni et al.

2004; Fetterly et al. 2013; Tate et al. 2014).


PKPD Understanding of TKIs as Case Studies

A few published examples of translational pharmacology of TKIs facilitated by

modeling and simulation approaches will now be reviewed highlighting how PKPD

modeling increased the translational value of available data and enabled data-driven

mechanistic interpretations.

Nonclinical PKPD relationships of crizotinib (PF02341066), an orally available

small-molecule TKI of multiple rTKs including anaplastic lymphoma kinase (ALK)

and mesenchymal-epithelial transition factor (MET), were characterized in athymic

nu/nu mice implanted with either H3122 non-small cell lung cancer (NSCLC) cells

or GTL16 gastric carcinoma cells (Yamazaki et al. 2008, 2012). Crizotinib maximal

plasma concentration in both xenograft models was observed earlier than the maximal inhibition of ALK and MET phosphorylation in tumors. The ALK and MET

inhibition was also sustained relative to the decline of crizotinib plasma concentrations. The observed time delay of ALK or MET inhibition in tumor relative to crizotinib plasma concentration was possibly due to rate-limiting distribution of crizotinib

from peripheral blood to the target tumors. Thus, the pharmacodynamic responses

for both ALK and MET were adequately modeled using a link model, which provided unbound EC50 values of 19 and 1.5 nM for ALK and MET inhibition, respectively. Tumor growth curves in the vehicle control groups were characterized by an

exponential tumor growth model either with or without a logistic function. Crizotinib

antitumor effect in both xenograft models was fitted reasonably well by a modified

indirect response model. The estimated unbound EC50 values were 20 and 17 nM for

ALK- and MET-driven xenograft models, respectively. Interestingly, the EC50 value

for antitumor effect for an ALK-driven xenograft model was comparable with the

ALK inhibition EC50, whereas the EC50 against MET-driven tumors was approximately tenfold higher than the EC50 value for MET inhibition. This implies that the

EC50 value for antitumor effect is roughly comparable to the EC90 value for MET

inhibition (13 nM unbound). Collectively, the PKPD modeling results suggest that

>50 % ALK inhibition would be required for a significant antitumor effect (>50 %

tumor growth inhibition), while near-complete MET inhibition (>90 %) would be


Pharmacokinetics and Pharmacodynamics of Tyrosine Kinase Inhibitors


Fig. 7.2 PKPD modeling summary of crizotinib-mediated target modulation and antitumor efficacy in human tumor xenograft models. Cp plasma concentration, F oral bioavailability, ka absorption rate constant, V volume of distribution, k elimination rate constant, t time after dosing, Ce

effect-site concentration, ke0 rate constant for equilibration with the effect site, E biomarker

response ratio to baseline (E0), EC50 concentration causing 50 % of maximum effect (Emax), T tumor

volume, R logistic function (1 − T/Tss), where Tss is a maximum sustainable tumor volume (R = 1 for

exponential growth model) (Reproduced with permission from Yamazaki S. et al., AAPS J 2013;


required for the same degree of antitumor effect. Thus, the crizotinib PKPD relationships of target modulation relative to tumor growth inhibition in nonclinical

models appear to be different between the two targets, suggesting that, to achieve

similar levels of antitumor effect in cancer patients, targeting ALK may be more

effective than targeting MET. Overall, the PKPD relationships among crizotinib

systemic exposures, target modulation, and antitumor efficacy in tumor xenograft

models were well characterized in a quantitative manner using mathematical PKPD

modeling, as summarized in Fig. 7.2.

Pictilisib (GDC0941) is a novel small-molecule inhibitor of phosphatidylinositol

3-kinase (PI3K). The PKPD relationships between the pictilisib plasma concentrations and pharmacodynamic biomarker responses of phosphorylated AKT and

phosphorylated proline-rich Akt substrate of 40 kDa (PRAS40) were characterized

in athymic nu/nu mice implanted with MCF7-1 breast carcinomas (Salphati et al.

2010). The indirect response model reasonably fit both the biomarker responses for

AKT and PRAS40 with total drug EC50 estimates of 0.36 and 0.29 μM, respectively.

The estimated EC50 value for AKT in the nonclinical model was consistent with that

(0.3 μM) in platelet-rich plasma from cancer patients (Sarker et al. 2009), suggesting pictilisib-mediated AKT responses were consistent between nonclinical models

and patients. The antitumor efficacy of pictilisib was also adequately characterized

by a modified indirect response model, based upon an exponential growth model in

a vehicle control group. The model-estimated concentrations required for tumor

stasis (i.e., 100 % tumor growth inhibition) was 0.3 μM, which was roughly comparable to the EC50 estimates for AKT and PRAS40 inhibition (0.36 and 0.29 μM,

respectively). Thus, the PKPD modeling results suggested that approximately 50 %

inhibition of AKT and PRAS40 phosphorylation would be associated with tumor

stasis in the nonclinical model.


A. Ruiz-Garcia and K. Yamazaki

An orally available selective inhibitor of ALK and ROS oncogene 1 (ROS1),

PF06463922, is a second-generation ALK inhibitor for crizotinib-resistant NSCLC

patients. The PKPD relationship of PF06463922 between the systemic exposure,

ALK inhibition in tumors, and antitumor efficacy was characterized in athymic

mice implanted with H3122 NSCLC cells expressing echinoderm microtubuleassociated protein-like 4 (EML4)-ALK mutation (EML4-ALKL1196M) (Yamazaki

et al. 2014, 2015). Interestingly, a dose-dependent rebound of ALK phosphorylation

was observed at 24–36 h post-dose (i.e., the ALK phosphorylation ratio was greater

than 1 in the treatment groups relative to the vehicle control group). In order to take

into account the observed rebound, a modulator was incorporated into the basic

indirect response model as a precursor. This allowed to estimate the in vivo potency,

unbound EC50 for ALK inhibition (36 nM), which was >twofold lower than the

estimated EC50 value by a simple indirect response model without a modulator

(84 nM). Based upon the difference in objective function values between these models, the indirect response model with a modulator fitted the time course of ALK

inhibition statistically better than the indirect response model without a modulator,

indicating the importance of selecting the appropriate PKPD model to accurately

characterize the PKPD relationship. Tumor growth curves in the xenograft control

groups with EML4-ALKL1196M and ROS1 were well characterized by an exponential

tumor growth model, without and with a logistic function, respectively. Tumor

growth inhibition by PF06463922 was then fitted adequately by a modified indirect

response model. The model-estimated unbound concentrations required for tumor

stasis were of 51 and 6.2 nM in the xenograft models with EML4-ALKL1196M and

ROS1, respectively. Thus, the unbound EC50 to EC60 estimates for ALK inhibition

(36–52 nM) roughly corresponded to the unbound tumor stasis concentration

(51 nM) in the xenograft models, suggesting that near 60 % ALK inhibition would

be required for tumor stasis.

The previous examples illustrated how translational research groups have

applied a two-step approach to characterize the PKPD relationships of TKIs in

nonclinical models. The PKPD relationships of TKIs for biomarker responses and

antitumor efficacy were separately characterized in parallel as a function of

plasma concentrations; subsequently, the efficacious concentrations of TKIs were

estimated by comparing the exposure-response relationships established between

plasma concentrations and biomarker responses and between plasma concentrations and antitumor efficacy. To facilitate translation, the plasma concentrations

associated with biomarker responses (e.g., >50 % inhibition), which lead to a

desired degree of antitumor efficacy (e.g., 50–100 % tumor growth inhibition) in

nonclinical models, could be considered and used as minimum target efficacious

concentrations in clinical trials such as phase I studies. Thus, PKPD modeling is

a key approach to quantitatively establish exposure-response relationships of

TKIs in nonclinical models and can greatly facilitate a deeper understanding of

translational pharmacology. This understanding can be used to make advancement decisions in the early development stage and also to guide trial designs and

dose adjustments in the clinic.


Pharmacokinetics and Pharmacodynamics of Tyrosine Kinase Inhibitors



Extrapolation of Antitumor Efficacy from Nonclinical

Models to the Clinical Setting

In drug discovery and development, the efficacious concentrations at the target site

of clinical drug candidates are routinely projected by characterizing a quantitative

PKPD relationship in nonclinical models, as described above. Projected efficacious

concentrations of TKIs can be then used as a surrogate marker in guiding the phase

I dose-escalation study, as well as establishing a recommended phase II dose and

dose schedule. Establishment of a quantitative exposure-response relationship

should be one of the main objectives of nonclinical in vivo TKI PKPD studies. For

instance, the PKPD relationship of crizotinib exposure, ALK and/or MET inhibition, and tumor growth inhibition was quantitatively characterized in human tumor

xenograft models using mathematical modeling as described above (Yamazaki et al.

2008, 2012; Yamazaki 2013). The PKPD modeling results in nonclinical models

suggest that 50 % ALK inhibition would be required for a significant antitumor

efficacy (i.e., >50 % tumor growth inhibition), whereas >90 % MET inhibition

would be required for the same degree of tumor growth inhibition. Accordingly, the

minimal target efficacious concentrations of crizotinib in patients with ALK- and

MET-positive tumors were projected as the steady-state trough concentrations

required for >50 % ALK inhibition (i.e., ALK EC50 = 19 nM free) and >90 % MET

inhibition (i.e., MET EC90 = 13 nM free), respectively. Following this analysis, the

clinical PKPD relationship of crizotinib in a phase I dose-escalation study (e.g., a

starting dose of 50 mg once daily to the highest dose of 300 mg twice daily) was

simulated based upon the clinically observed/predicted human PK parameters and

the PD parameters obtained from nonclinical models. Crizotinib-mediated ALK

and MET inhibition in patient tumors at the recommended phase II dose, twice daily

doses of crizotinib 250 mg (500 mg/day), was projected to be >75 % and >95 %,

respectively, which was higher than the projected minimal target modulations of

50 % and 90 %, respectively. The projection of the expected PKPD relationship at a

recommended phase II dose could be critical for a go/no-go decision. Thus, despite

the lack of crizotinib-mediated ALK- or MET-related biomarker data in cancer

patients, the modeling and simulation approach was applied to phase I doseescalation study to support the selection of recommended phase II dose associated

with systemic exposures; this dosing regimen later demonstrated promising clinical

responses in cancer patients (Kwak et al. 2010; Ou et al. 2011; Ou 2012).

An interesting PKPD modeling analysis has been reported for molecularly targeted and cytotoxic agents (Wong et al. 2012). This analysis established a relationship between clinically observed plasma drug concentrations and antitumor efficacy

in nonclinical xenograft/allograft models. The authors first performed PKPD

modeling to characterize the relationship between plasma concentrations of anticancer agents and antitumor efficacy in nonclinical models. Subsequently, a PKPD simulation with the PD parameter estimates in nonclinical models was carried out at

clinically relevant dosing regimen, yielding plasma concentrations comparable to

clinically observed exposures. In other words, antitumor efficacy of each agent in


A. Ruiz-Garcia and K. Yamazaki

nonclinical models was simulated at clinically relevant plasma concentrations with

the PD parameters obtained from nonclinical models. The results suggest that anticancer agents showing >60 % tumor growth inhibition at clinically relevant exposures in nonclinical models likely lead to promising responses in the clinic. Despite

these encouraging observations, it should be noted that the degree of target tumor

growth inhibition could depend upon several factors, such as the nonclinical xenograft model used, the maximum attainable tumor growth inhibition, the target modulation vs. tumor growth inhibition relationship, and the specifics of the clinical

indication. These factors should be carefully considered to project a minimal target

efficacious concentration based on a target tumor growth inhibition. In some cases,

tumor stasis or even tumor regression could be appropriate for a minimal target antitumor efficacy. As an example, tumor stasis concentration has been reported as a

minimal target efficacious concentration of the second-generation ALK inhibitor,

PF064639322, in NSCLC patients with EML4-ALK rearrangements with and without ALK mutations (Yamazaki et al. 2014). The PKPD modeling results showed that

the unbound EC50 to EC60 estimates for ALK inhibition (36–52 nM) roughly corresponded to the unbound tumor stasis concentration (51 nM) in nonclinical xenograft

models, suggesting that near 60 % ALK inhibition would be required for tumor stasis

as described above. Accordingly, the unbound EC60 for ALK inhibition (~50 nM) has

been proposed to be a minimum target efficacious concentration of PF06463922 in

NSCLC patients with EML4-ALK rearrangements. In addition, the unbound EC75

estimate (100 nM) for PF06463922-mediated ALK inhibition has been proposed to

be a target plasma concentration for crizotinib-resistant NSCLC patients. This was

dimensioned against the drug levels required to achieve equivalent antitumor efficacy as was observed in crizotinib-sensitive NSCLC patients with wild-type ALK

rearrangements. It reflects the (previously described) projected >75 % crizotinibmediated ALK inhibition in patients at the clinically recommended dose of 250 mg

twice daily. It remains to be seen whether the projection of efficacious concentrations

of PF06463922 in patients will be consistent with clinical responses, since

PF06463922 has just recently entered a phase I dose-escalation study.

The projected minimal efficacious concentrations of molecularly targeted agents,

such as TKIs, generally target steady-state systemic exposures required to achieve

promising efficacy in cancer patients. Therefore, the projected minimal efficacious

concentrations of TKIs are often used as surrogate markers of antitumor efficacy in

clinical studies. In particular, phase I studies are generally conducted in a manner of

dose escalation, to determine safety profiles including maximal tolerated dose

(MTD), dose-limiting toxicities, PK profiles, and the recommended phase II dose

(RP2D). Operationally, whether plasma concentrations of TKIs would reach projected minimal efficacious concentrations in phase I studies could be the basis of a

go/no-go decision. Clinical PKPD relationships of systemic exposure of molecularly targeted agents to target modulation and/or its surrogate biomarker response

(e.g., proof of mechanism) could in principle be established in phase I studies in an

expanded cohort setting of selected patient populations. However, pharmacodynamic biomarker measurements in cancer patients are not common, since tumor

biopsy samples, especially serial samples, are difficult to obtain from patients.

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