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3 Summary: Applying Tumor Growth Models to Clinical Development

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11  Modeling Tumor Growth in Animals and Humans: An Evolutionary Approach



229



started to gain momentum. In this chapter, an alternative formulation of tumor

kinetic modeling has been put forth where the tumor growth and response is framed

as an evolutionary process subject to selection pressure due to the treatment of interest. (In practice, mixtures of models may be required to uniquely determine individual parameters). Notably—and with direct implications for how cancer biologists

think about translating results from Phase II to Phase III-evolutionary modeling

suggests that the survival benefit due to a treatment depends strongly on initial

growth rate, resistant fraction, and resistant growth rate but negligibly on initial

response.



3  Conclusions

It has been shown in this chapter that evolutionary models are relatively simple but

powerful and flexible, and have the advantage of providing mechanistically relevant

insights as well as providing the ability to predict downstream response. Framing the

early clinical development paradigm in terms of evolutionary models provides a way

of thinking about the process that is more consistent with the emerging picture of

tumor biology, and allows, in many cases, for cleaner and more statistically sound

answers to the three questions outlined at the beginning of this section. While much

still remains to be learned about the fundamental mechanisms and processes of cancer, using more empirical and simple models that capture our limited understanding

of the biology may lead to a more robust development process.



Appendix

When Can Tumor PK Be Replaced with Cave/AUC?

It is common pharmacometrics practice to use full time course of PK to drive the PD

model, in this case the tumor kinetics. However, in the case where PK oscillates on

a rapid time scale of say hours for daily dosing, and the tumor kinetics are measured

over the course of months, this level of temporal resolution is about as necessary

(and potentially computationally costly) as modeling the peristaltic contractions of

pill swallowing to predict PK over the next 24 h. If, however, the concentration-­

effect relationship is approximately linear over the range of relevant concentrations,

say, g ( c ) = g max (1 − mc (t )) , then for a given clone (suppressing clone subscript i):

t



òg ( c ( t))dt



N (t ) = N 0e 0



t



t



max

òg (1- mc ( t))dt



= N 0e 0



t

é

ù

g max êt - m c ( t ) dt ú

ê

ú

0

0

ë

û



=N e



ò



= N 0e



g max ( t - m AUC ( t ) )



(11.25)





where AUC ( t ) º òc ( t ) dt is the cumulative area under the concentration curve

from time 0 to t. 0



230



D.C. Bottino and A. Chakravarty



 umor Growth Is Dominated Initially by the More Frequent

T

Clone and Eventually by the Resistant Clone

Note that if the normalized growth rate for the two-population model is examined



˜







N (t )

N0



= (1 - f R ) e gs



min



t



+ f R e gR t



(11.26)





The rate of change of Ñ at any time t is of course given by its first derivative:

˜ ¢







N ( t ) = (1 - f R ) gsmin e gs



min



t



+ f R g R e gR t







(11.27)



This implies that initially,

˜ ¢







N ( t ~ 0 ) » (1 - f R ) gsmin + f R g R







(11.28)



In other words, the kinetics are dominated by which clone is more prevalent at time

zero (assuming gsmin and gR are of similar magnitude).

At longer times, however, since g smin < 0 , the sensitive clone term goes to zero

while the resistant clone dominates, ie,

˜ ¢







N ( t  1) » f R g R e gR t







(11.29)



That is, the derivative of the tumor burden is dominated by the derivative of the

resistant term.



Other Commonly Used Tumor Kinetic Model Formulations

Long lists of tumor kinetics models that have been used in a clinical nonlinear

mixed effects context are available in the literature (Ribba et al. 2014). Two of the

more prevalent models in the context of our evolutionary modeling framework will

be discussed.

The Claret/Bruno TGI model looks like this:







dN

= g - k ( c ) e -lt N

dt





(



)



(11.30)



where g is the untreated growth rate, k(c) is the kill rate as a function of drug

concentration, and λ is the decay rate of the drug effect (Claret et al. 2013a, b).

Expressed in our notation under similar concentration averaging assumptions the

equation becomes:



11  Modeling Tumor Growth in Animals and Humans: An Evolutionary Approach







(



)



dN

= gSmax - gSmax - gSmin e -lt N

dt





(



)



231



(11.31)



This approximation has an analytic solution:







1

é

N ( t ) = N 0 exp ê gSmax t + gSmax - gSmin

l

ë



(



) (e



- lt



ù

-1 ú

û



)



(11.32)



This model has one less parameter than the evolutionary model because it assumes

that the pretreatment growth rate is equal to the tumor growth rate at relapse. While

one might assert that g R < gSmax at least in patients with an initial response, the

authors are aware of no evidence to support the assumption that the initial growth

rate equals the relapse rate, i.e., g R = gSmax . Again, acquisition of pre-baseline tumor

scans may shed light on this question.

That said, in a world where NONMEM convergence or failure is considered to

be an acceptable acceptance or rejection criterion for a given structural model, the

Claret/Bruno model likely has more readily estimable parameters, which may suffice when interpretability of model parameters is not a concern. Additionally many

papers are already written relating parameters from this model to OS, so this model

might be useful for predicting survival in an indication for which there is already a

published tumor kinetics-to-OS relationship.

Another popular tumor kinetic model is of the form:







min

N

= e g S t + e g R t − 1.

N0



(11.33)





This expression is nearly equivalent to the special case of the evolutionary model

where f R = 0.5 . While this model has also been used successfully to relate tumor

growth rates to survival (Stein et al. 2008), the authors are not aware of any data

supporting the assumption that resistant cells make up half the tumor burden at start

of treatment.



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Chapter 12



Practical Considerations for Clinical

Pharmacology in Drug Development:

A Survey of 44 FDA Oncology Approvals

Danny R. Howard



Abstract This chapter presents the practical challenges facing the oncology clinical

pharmacologist by surveying the cancer chemotherapies approved by the FDA in

the period between January 2009 and June 2015. For each new medicine, the contents of the review summaries published by the FDA in the Drugs@FDA database

at the time of first approval are examined. The comments of reviewers in the areas

of subject and patient selection, dose and regimen justification, characterization of

food and pH-altering drug interactions, evaluation of dose adjustment for organ

impaired populations, and QTc assessment of new oncology therapeutics are presented. The information gleaned from the 44 approvals for new oncology drugs

during this period provides the reader with an insight into the expectations and

requirements for initial approval and provide practical information for how regulatory guidance is applied in clinical pharmacology.

Keywords Regulatory • Food and Drug Administration • Package insert • Product

label • Clinical development



1



Introduction



The commonly understood approach for the development of new drugs is frequently

presented as follows: a new molecule is vetted for gross pharmacology and safety in

preclinical studies, enters Phase I to determine the clinical safety, tolerability, and

maximum tolerated dose (MTD), proceeds to Phase II where, if the mechanism of

action and a measurable stabilization or regression of disease are verified, it may



D.R. Howard (*)

Novartis Pharmaceuticals, Oncology Clinical Pharmacology,

One Health Plaza, East Hanover, NJ 07936-1080, USA

e-mail: dan.howard@novartis.com

© Springer International Publishing Switzerland 2016

P.L. Bonate, D.R. Howard (eds.), Pharmacokinetics in Drug Development,

DOI 10.1007/978-3-319-39053-6_12



237



238



D.R. Howard



enter Phase III studies and can be compared to the established standard of care.

Phase I defines the pharmacokinetics and safety, Phase II optimizes dose, regimen,

and indication selection, and Phase III confirms these findings and defines the overall benefit–risk ratios. This simple paradigm is appealing because it is general

enough to be applied to any therapeutic area and because the “Phase I-Phase

II-Phase III” jargon is a shared lexicon among drug development scientists and

regulators worldwide.

Today, however, the limitations of the Phase-based mentality in drug development are more apparent than ever before. This is especially true in the therapeutic

area of oncology where the life-threatening nature of the disease and often limited

treatment options create an urgent unmet medical need. New review strategies, like

the United States’ Food and Drug Administration’s Breakthrough Therapy

Designation (Guidance for Industry: Expedited Programs for Serious Conditions—

Drugs and Biologics 2014) combined with the accelerated and priority approval

designations have allowed for safety, efficacy, dose, pharmacokinetics, indication,

treatment population, and marketable formulation to be defined as early as the firstin-human study. Overall, fewer studies are being required for initial approvals—83 % of new oncology drugs approvals between 2006 and 2011 were based on

a single study (Martell et al. 2013). These pressures require development scientists

to reexamine the old development schemes. There are now several examples for

expedited review and drug approval obtained for drugs given the Breakthrough

Therapy Designation. Given the extraordinary benefit the Breakthrough Therapy

designation brings to patients, and the appeal it has for pharmaceutical companies

whose mission it is to bring new medicines to them, we can safety assume we are on

the cusp of realizing a new drug development paradigm.

The very nature of our drug treatments for oncology has changed significantly

over the last 20 years. Cytotoxic agents, which dominated the chemotherapeutic

treatment landscape for decades, are being replaced by small-molecules and monoclonal antibodies targeted to specifically inhibit oncogene function or promote suppression directly at the tumor. Prior to 2000, only three targeted agents were

available to oncologists: one small-molecule (imatinib), one antibody (trastuzumab), and the antibody-drug conjugate, gemtuzumab ozogamicin, which was

withdrawn from the US market in 2010 when a confirmatory trial failed to demonstrate clinical benefit (Petersdorf et al. 2013). Today targeted agents make up greater

than 80 % of all new oncology drug approvals since 2009. Unlike cytotoxic agents

which inhibit all actively dividing cells, targeted agents interfere with tumor cell

signaling pathways to block cell proliferation, growth, and metastases.

Immunotherapies meant to exploit the body’s own defenses by upregulating the

immune system, blocking interactions of cellular surface proteins which interfere

with T-cell response, or genetically modifying host immune system components to

target tumor cells are fast becoming the next major therapeutic advancement. Nearly

30 % of all approvals since 2009 have been monoclonal antibodies.

We have also seen a significant change in the effectiveness of our cancer therapies. Patients are living longer. Chemotherapy for some cancers, including forms

of leukemia, lymphoma, small-cell lung cancer, and ovarian cancers, are curative.



12



Practical Considerations for Clinical Pharmacology in Drug Development…



239



In just under 40 years, the 10-year relative survival rate for patients diagnosed with

leukemia has doubled from under 24 % to nearly 50 % and the life expectancy of a

patient diagnosed with leukemia has extended by 40 years (Howlader et al. 2015).

Significant progression-free survival has also been observed in renal, breast, liver,

and gastrointestinal cancers. Clinical studies with anti-PD1, CTLA-4, and CDK4/6

inhibitors have already shown great promise for extending the lives of patients with

melanoma, lung, and breast cancers. We now recognize cancer as a multi-organ

disease where genetic coding errors may be targeted, suppressed, or overwritten.

The primary focus of the industrial clinical pharmacologist has always been to

characterize the sources of variability to drug exposure and response, optimize dose

and regimen within an indication, and provide appropriate labeling instructions to

control or accommodate the variability. However, for indiscriminately cytotoxic

drug therapies, or when the disease is acute and terminal, dose-optimization and

characterization of variability due to drug interactions, demographics, or comorbidities may have little meaning for both doctor and patient. Many cytotoxic agents

are intravenous injections; the formulations are rarely complex, and could be optimized in the laboratory, not in the clinic. The issues of clinical pharmacology are

lesser importance for cytotoxic agents. However, the recent emphasis on targeted

and oral agents, which may be taken by patients over long periods of time, has

increased the importance of clinical pharmacology in oncology drug development.

The expectations of regulatory bodies can be expected to evolve accordingly

(Minasian et al. 2014).

This chapter will present the practical challenges facing the oncology clinical

pharmacologist by surveying the cancer chemotherapies approved by the FDA in

the period between January 2009 and June 2015. For each new medicine, the

contents of the Drugs@FDA database are examined for practical insights to the

expectations of reviewers in the areas of subject selection, dose and regimen justification, characterization of food and pH-altering drug interactions, evaluation of

dose adjustment for organ impaired populations, and QTc assessment of new oncology

therapeutics.



2



The Approval Database



The drugs reviewed for this exercise include all new medicines with a primary indication to treat cancer, approved by the Center of Drug Evaluation and Research

(CDER) between January 2009 and June 2015. It excludes approvals for new indications or new formulations of previously approved therapeutics. Furthermore,

since the focus is on drugs intended to treat disease, approvals for supportive care

and diagnostic agents were also excluded. The information contained in this chapter

is based on the published contents of the FDA Approval Letter, Label, Clinical

Pharmacology and Biopharmaceutics Review, Medical Review and Interdisciplinary

Review Team for QT Studies Consultation for each drug published on the FDA

website at the time of drug approval (Drugs@FDA). The purpose of this survey was



240



D.R. Howard



to understand the decisions made at the time of review which led to the approval,

and the focus of this survey is on the information presented for review by the agency

at that time. Any additional or follow-up information from sources published after

the approval are noted and referenced.

As shown in Table 12.1, this review covers the 44 new oncology approvals

granted by the Food and Drug Administration during this time period. This group

was dominated by small molecules and targeted therapeutics, with few new cytotoxic drug approvals. Thirty-two of the new approvals were for targeted therapeutic

agents, two were hormone therapies, and ten were cytotoxics. Of the targeted therapeutics, 19 were small molecules, nine were antibodies, two were antibody-drug

conjugates, and two were protein/peptides.

Most of the oncology drugs approved during this period (77 %) were given priority, accelerated or breakthrough therapy status. The high rate has been a consistent

feature of oncology drug development for over two decades (DiMasi and Grabowski

2007). Priority review is granted for drugs expected to provide significant improvement in safety or efficacy in the treatment of serious conditions. The goal is to provide for the review of priority drugs in a 6 month timeframe, or about 4 months

faster than standard review times of approximately 10 months. Under the agency’s

accelerated approval program, the approval may be based on a surrogate endpoint

reasonably likely to predict the clinical benefit in a serious or life-threatening disease, and this designation is intended to provide earlier access to promising new

drugs while confirmatory clinical trials are being performed. The Breakthrough

Therapy Designation was established in 2012 to provide for the fast approval of

treatments intended for serious or life-threatening illnesses. It provides all of the

benefits associated with the other designations, plus the addition of more intensive

FDA guidance, communication, collaboration, and interaction.

The first breakthrough therapy designate, Gazyva (obinutuzumab), was approved

in November 2013. Since then, there have been 15 new oncology drugs approved

and eight of these have been breakthrough therapy designates.



2.1



Use of Patients and/or Healthy Volunteers



For reasons of safety, clinical evaluation of cytotoxic oncology agents necessarily

excluded the participation of healthy volunteers and restricted testing only to those

patients for whom the risks associated with the treatment might be off-set by the

potential benefits. As indicated previously, targeted agents—small molecules,

monoclonal antibodies, and antibody-drug conjugates—currently dominate the new

drug approvals.

Many of these targeted agents are neither genotoxic nor mutagenic, and restrictions for the participation of health volunteers in clinical pharmacology studies do

not exist. But, while it may be possible to initiate first-in-human dose-escalation and

tolerance studies in healthy volunteers, this approach was rarely employed. In fact,

of the last 44 new oncology drugs approved by the FDA since 2009, only idelalisib



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