Drug Discovery Today Volume Issue 2013 [Doi 10.1016_j.drudis.2013.05.012] Visser, Sandra a.G.;...

Reviews�POSTSCREEN

Drug Discovery Today � Volume 00, Number 00 � July 2013 REVIEWS

Model-based drug discovery:implementation and impact

Sandra A.G. Visser1, Malin Aurell2, Rhys D.O. Jones3, Virna J.A. Schuck4,Ann-Charlotte Egnell5, Sheila A. Peters6, Lena Brynne7, James W.T. Yates3,Rasmus Jansson-Lofmark5, Beesan Tan4, Marie Cooke8, Simon T. Barry9,Andrew Hughes10 and Ulf Bredberg11

1Global Drug Metabolism and Pharmacokinetics, Innovative Medicines, AstraZeneca, Sodertalje, Sweden2 Personalized Healthcare & Biomarkers, AstraZeneca, Molndal, Sweden3Modeling and Simulation, Oncology Innovative Medicines, AstraZeneca, Alderley Park, UK4Modeling and Simulation, Infection innovative Medicine, AstraZeneca, Waltham, MA, USA5Modeling and Simulation, Cardiovascular and Metabolic Disease Innovative Medicines, AstraZeneca, Molndal, Sweden6Modeling and Simulation, Respiratory, Inflammation & Autoimmunity, Innovative Medicines, AstraZeneca, Molndal, Sweden7 Translational Science, Central Nervous System and Pain Innovative Medicines, AstraZeneca, Sodertalje, Sweden8 Research and Discovery Informatics, AstraZeneca, Alderley Park, UK9Bioscience Oncology Innovative Medicines, AstraZeneca, Alderley Park, UK10Global Medicines Development Oncology, Alderley Park, UK11Drug Metabolism and Pharmacokinetics Cardiovascular and Metabolic Disease Innovative Medicines, AstraZeneca, Molndal, Sweden

Model-based drug discovery (MBDDx) aims to build and continuously improve the quantitative

understanding of the relation between drug exposure (target engagement) efficacy and safety, to support

target validation; to define compound property criteria for lead optimization and safety margins; to set

the starting dose; and to predict human dose and scheduling for clinical candidates alone, or in

combination with other medicines. AstraZeneca has systematically implemented MBDDx within all

drug discovery programs, with a focused investment to build a preclinical modeling and simulation

capability and an in vivo information platform and architecture, the implementation, impact and

learning of which are discussed here.

IntroductionIt is well established that the drug industry faces an enormous

challenge in the form of late-stage compound attrition [1–3]. Some

70% of recent failures in Phases 2 and 3 of drug development,

which are associated with the most significant costs, have been

attributed to efficacy or safety reasons [4,5]. A detailed review by

Pfizer into the root causes of this problem, demonstrated a link

between a higher probability of success in Phase 2 and having an

integrated quantitative understanding of the fundamental phar-

macokinetic–pharmacodynamic (PKPD) principles, namely expo-

sure at the site of action, target binding and expression of

Corresponding author:. Bredberg, U. ([email protected]),

([email protected])

1359-6446/06/$ - see front matter � 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.drudis.

functional pharmacological activity at the site of action [6]. Simi-

lar results were obtained within AstraZeneca in a comprehensive

longitudinal review of company projects between 2005 and 2010

(unpublished results). These findings advocate a systematic appli-

cation of PKPD principles, also known as quantitative pharmacol-

ogy, throughout the drug discovery and development value chain,

and to select candidates with more confidence that they will be

able to demonstrate the biological and translational hypothesis in

clinical development. This premise should shift compound attri-

tion to discovery or to the earlier clinical stages of development by

ensuring more robustly designed studies delivering against go/no

go criteria.

The value of a model-based approach during drug development

for improved efficiency and decision-making has long been recog-

2013.05.012 www.drugdiscoverytoday.com 1

mailto:[email protected]

mailto:[email protected]

http://dx.doi.org/10.1016/j.drudis.2013.05.012

REVIEWS Drug Discovery Today � Volume 00, Number 00 � July 2013

DRUDIS-1186; No of Pages 12

Review

s�P

OSTSCREEN

nized [7–12]. However, it is only in recent years that the applica-

tion of quantitative principles to early clinical and preclinical

development phases has been demonstrated in the literature

[13–15]. Additionally, regulatory agencies are increasingly advo-

cating the need for model-based approaches during late-stage [16–

18] and early-stage development [3,19] for informed decision-

making and optimizing trial design. Despite these advances, case

studies on the application of model-based principles in the pre-

clinical phase have been mainly confined to supporting academic

research and little evidence exists for how companies have system-

atically implemented a model-based approach into drug discovery

[20–24]. Recently, AstraZeneca has taken a strategic initiative to

promote the use of quantitative methods within drug projects at

all stages. In this initiative, a specific cross-functional global work-

stream was formed with a focus on the preclinical stage. This

workstream laid out a MBDDx approach that aimed to: (i) embed

and implement the systematic application of quantitative phar-

macology to drug discovery from target validation, via candidate

selection, to proof of mechanism and concept; (ii) build a dedi-

cated and state-of-the-art preclinical modeling and simulation

(M&S) capability; and (iii) enable rapid access to cross-functional

data and knowledge at the individual animal and observation level

(Fig. 1). A US$13 million investment for a 3-year plan was

approved to build the preclinical modeling capability through a

combination of retraining, recruitment and establishment of stra-

tegic external collaborations, as well as the development of an in

vivo data information platform.

A successful implementation of MBDDx can support: (i) target

validation and the elucidation of the biological hypothesis; (ii)

Implementation phase Busines

Training and reskilling

External academic collaborations and translational biomarker and PKPD Science Postdoc p

Leadership & governance engagement

Awareness workshops & activities

Development in vivo data contract and user requirements

Data ramp-up and user acceptance training

Capability build

Quantitativeship & governance engagement

Implementation phase

Awareness workshops & activities

ntitative pharmacology yyyyyyyststststststs raraarararaateteteteetegygygygygygyystrategy

Information plplplplplplpp atatatatataatfofofofofofofoormrmrmrmrmmrmm platform

Recruitment M&S capability

Key performance indicator definitions Progress

Early stage clinical data pi

Ownership & project operating model

Strategic externalization activities

Talent management, succe

Advanced training

Pilot results & learning

Technical delivery of in vivo data store and query tool Development of integration

Maintenance data contract

Routine upload of all in viv

Sharing impact case studie

Pull from governance and

Refinement of operating m

FIG. 1

Model-based drug discovery (MBDDx) aims at: (i) a systematic application of quantit

candidate selection, to proof of mechanism and concept; (ii) building dedicated andenabling rapid access to cross-functional data and knowledge at the individual a

implementation phase and business as usual. The variety of impacts of the various

2 www.drugdiscoverytoday.com

optimization of experimental design and definition of compound

property criteria for lead optimization; (iii) the defining of safety

margins and setting the starting dose; and (iv) predicting human

dose, optimal dosing schedule and clinical study design either

alone or in combination with other medicines. Once embedded

and implemented into drug discovery, MBDDx can underpin key

investment decisions in drug discovery in the same way that

Model-based drug development (MBDD) approaches support clin-

ical decisions. MBDDx provides a paradigm to integrate preclinical

information to back translate clinical results to confirm and refine

the biological hypothesis and, thus, influence future experiments

in discovery and development. With this in mind, MBDDx should

not be regarded as a stand-alone activity, but should be seen as an

essential prerequisite to MBDD, with a particular emphasis on

building dedicated resources and multidisciplinary delivery within

drug discovery. By taking this holistic approach, portfolios should

benefit from having a better understanding of the probability of

success of a drug project and being able to identify more easily

high-risk projects earlier. Portfolio and project leaders can in turn

focus appropriate resources on frontloading experiments to miti-

gate risk. In the following sections, we describe the MBDDx

approach and the lessons learnt during the implementation and

operational phases. We also highlight the importance of having

access to dedicated, disease area-focused state-of-the-art preclini-

cal M&S capability, and the development of a preclinical informa-

tion platform enabling rapid access to cross-functional data and

knowledge. Finally, we provide case studies exemplifying the

impact of the MBDDx approach on decision-making in drug

discovery.

Impacts as usual

rograms

Progress

lots

ssion planning

New methodology / Models / Future capability

Build and retention of key capability and talent

Development of capability

Flexibility in resourcing , state of the art methodologies

services

o data

Clear ownership and accountability

Rational governance and investment decision making

Demonstration of impact to business

Increased awareness, cultural change, best practices

project

odel

Globally wide In vivo data base and exploitation tool

Understanding blockers and investment areas

All data accessible for full exploitation and acceptance

Global standards for bench scientist and end-user

Drug Discovery Today

ative pharmacology to the drug discovery portfolio from target validation, via

state-of-the-art preclinical modeling and simulation (M&S) capability; and (iii)nimal and observation level. Focus and activities were different during the

activities is listed. Abbreviation: PKPD, pharmacokinetics–pharmacodynamics.




Quantitative pharmacology strategy in drug discoveryThe decision to initiate drug discovery activities around a specific

pharmacological target is based on a hypothesis linking the phar-

macological target to the disease in question. Crucial relations

involved in the cascade of events from target modulation to

disease modification need to be established to provide a platform

of biological evidence that develops in line with the expectations

of each phase of investment. Ultimately, the aim is to select a

compound with the right properties for clinical development, and

a clear view on the optimal doses and dose scheduling required for

testing the hypothesis in the clinical setting. This demands a

quantitative PKPD understanding of the link between the dose,

systemic and local drug exposure and the effect on the disease,

including the key intermediate steps involved in the biochemical

and physiological processes (Box 1). The PK of the compound and

its interaction with the target (target affinity and intrinsic efficacy)

are compound-specific properties, whereas the biochemical and

physiological processes downstream of the target are, by defini-

tion, compound independent and determined by the biological

attributes of the system [25,26]. Robust and meaningful biomarker

data, either proximal biomarkers (reflecting direct target modula-

tion) or distal biomarkers (reflecting pathway or disease modifica-

tion), are essential when building this quantitative insight [27–29].

A conceptual framework for biomarker classification has been

adopted to create a common terminology within AstraZeneca (Box

1, adapted from [27]). Fig. 1 (Box 1) is a schematic and simplified

way of illustrating the biological hypothesis. The circles illustrate

the different types of biomarkers (Type 1–6) and the yellow arrows

represent the quantitative relations between the various biomar-

kers and between species. Using this nomenclature, MBDDx aims

to create a quantitative understanding between: (i) drug exposure

(Type 1) and target engagement (proof of mechanism, Type 2, 3

and/or 4); and (ii) between target engagement and effect on

pathophysiology (proof of principle and/or concept, Type 5

and/or 6) in animals and its extrapolation to humans. The antici-

pated therapeutic concentration in the right patient population

(Type 0) and associated dose and schedule could be predicted

when this information is integrated with translational knowledge

(e.g., target expression, potency at the target and the relative

importance of the target in the disease). For safety measures, a

similar approach can be applied, enabling full integration of data

to estimate safety margins and therapeutic index. A common

biomarker nomenclature facilitates the discussion between scien-

tists from different functions when defining the translational

biomarker and quantitative M&S strategy for preclinical activities

as well as clinical proof of mechanism and proof of principle and/

or concept studies. It also aids the visualization of the knowledge

gaps, leading to strategic investment in biomarkers, techniques

and modeling activities. In addition, it can provide greater trans-

parency at a portfolio level for what is known and can be measured

against the biological hypothesis, thereby facilitating a more

consistent risk assessment across projects underpinning portfolio

and investment decisions.

In Box 1, general aspirations and guidance for different phases

of a project, from target validation, lead generation and optimiza-

tion, to candidate selection, are summarized. Along this value

chain, the knowledge base is continuously expanding via a learn-

and-confirm paradigm, where integration of high-quality M&S

support and biological data is essential to frame and answer the

right questions and articulate the key assumptions in the biolo-

gical hypothesis [30,31]. Doing the right experiments is an essen-

tial facet of MBDDx and, therefore, it is important that all

experiments are preceded with in silico simulations, based on

the current knowledge, to ensure an optimal experimental design.

Experimental data will subsequently confirm, refine or change the

biological hypothesis and quantitative understanding. First-in-

class targets without clinical precedence will carry risk until clin-

ical testing, with respect to how translatable the biological hypoth-

esis is and how appropriate the biomarkers being measured are.

Nevertheless, it is still important to ensure that sufficient (and

possibly substantial) investment is made to build assays to char-

acterize the drug- and system-specific properties to elucidate the

biological hypothesis and build a quantitative understanding to

predict the dose, schedule and therapeutic index. Conversely, for

targets that have already been tested in the clinic, a wealth of

information about system properties, biomarker behavior and

translatability obtained with comparators might be available that

should be utilized, to improve the confidence in the dose and

schedule predictions.

MBDDx can be applied to all phases of drug discovery; devel-

opment of a quantitative understanding of the target engagement

and disease biomarkers in addition to appropriate experimental

designs that take into account random or systematic variability in

the biomarkers, potential temporal and dose–response relations,

and appropriate induction of disease are important aspects to take

into consideration [32–35]. Without studying these characteristics

sufficiently, and/or if studies are poorly designed, experiments are

likely to produce inconclusive data or misleading conclusions.

Thus, without these fundamentals in place, a project can carry

substantial risk, and might lead to a lower probability of success, or

require additional time and money to address later in the clinic.

With upfront investment in defining the systems characteristics of

the biomarker model, the experimental design can be optimized

towards the (lean) characterization of drug-specific properties and,

thus, improve the efficiency of the lead generation and optimiza-

tion screening cascade. Additionally, integration and (re-)use of all

sources of data is a crucial component of the quantitative model;

for example, the back-translation of available clinical data or

deriving in vitro and/or in vivo preclinical data on reference or

competitor compounds aiming for the same indication. Any addi-

tional information on interspecies differences with regards to the

target should be integrated and confirmed to increase confidence

in translation.

Depending on therapeutic areas, there are different challenges

and opportunities in the application of MBDDx. In current neu-

roscience research, many drug discovery projects are first in class,

with limited or no availability of translational disease models, such

as Alzheimer’s disease and chronic pain. Progression of com-

pounds in these areas can carry high risk. For infectious diseases,

the target engagement markers are, strictly speaking, not mea-

sured in humans, but in the bacteria or virus. This makes it even

more important to have a measure for target site exposure, to

ensure that target engagement with bacteria or virus can be met in

the appropriate tissue. In oncology, the comparison to standard of

care, irrespective of the mechanism of action, is important regard-

less of whether the compound in development is to be applied

www.drugdiscoverytoday.com 3



BOX 1

The quantitative pharmacology strategy uses a commonnomenclature for biomarker classificationUnderstanding the relation between drug dose and outcomerequires understanding of the PKPD of the drug. These principlescan be regarded as a causal chain of events from drug dosing,distribution to the target site and elimination from the body,subsequent binding and modulation of the target related to aneffect on disease and pathophysiology, measured as clinicaloutcome (Figure Ia). MBDDx advocates the use of a commonnomenclature according to the biomarker classification system(adapted from [27], Figure Ib). Figure Ib mirrors in a simplified waythe biological hypothesis implied and the circles illustrate thedifferent type of biomarkers (Type 1–6). The yellow arrowsrepresent the quantitative relation between the biomarkers andspecies. This includes both equilibrium relations as well as

temporal aspects (e.g., a time delay between PK and the measuredbiomarker). In drug discovery projects, these mathematicalrelations need not connect every step in the process, but canquantify, for example, Type 1 in relation to Type 3 biomarkers. Inthis nomenclature, PoM markers are those that define the degreeand duration of target engagement sufficient for viable hypothesistesting (Type 2, 3 and/or 4). PoP markers are those that measurebeneficial effect on targeted disease process or pathophysiology(Type 5), whereas PoC markers are Type 6 and/or Type 5. Type 0markers reflect patient stratification markers for personalizedhealth care (PHC). Generic quantitative pharmacology aspirationsand criteria at different stages in drug discovery for thequantitative characterization of these biomarkers are listed inFigure Ic.

Type 0 Biomarker that determines the disease state or the potential for therapeutic response or patient stratification (e.g., genotype or phenotype)

Type 1 The PK of the compound; typically unbound plasma concentrations and/or target site exposure

Type 2 Target occupancy via a direct measurement of receptor binding.(e.g., PET, autoradiography)

Type 3 An immediate biochemical response as a result of the interaction with the target (e.g., measure of signal transduction or of an enzyme product)

Type 4A A physiological or tissue response directly linked to the pathophysiology

Type 4B Parallel pharmacology driven through the same target but not directly linked to the pathophysiology (e.g., different tissues, such as central versus peripheral)

Type 5 A biomarker of the pathophysiology (e.g. disease marker)

Type 6 Clinical measure of the outcome in a patient population approved by regulators (e.g., pain relief)

Lead generation (LG) • Evaluation and selection of appropriate target

engagement biomarker (Type 2, 3 or 4) and optimization of PKPD study design

• Use reference or lead compounds and target engagement biomarker to establish relation between in vivo and in vitro potency

• Establish the level of target engagement required for meaningful efficacy on the disease (Type 5) biomarker

Target validation (TV) • Translational plan outlining development and

evaluation of appropriate biomarkers to build PKPD understanding

• If in vivo target validation model and a reference compound are available, apply PKPD principles to study design and ensure a sufficient duration and level of systemic unbound exposure relative to the in vitro potency while considering target class

Lead optimization (LO) and candidate selection • Clinical candidate criteria should be defined at start of LO based on

quantitative PKPD relations established during LG • Refinement of key relations with higher quality compounds • Target engagement PKPD as a driver for compound optimization • For clinical candidate compound: estimate therapeutic concentration

time profile based on the PKPD relation developed in preclinical species, and translation knowledge, such as differences in PK, target potency and system properties

• Integration of PKPD for safety parameters to assess safety margin

Generic MBDDx aspirations and criteria for drug discovery phases

Animal

Human

Type 5 Pathophysiology

or disease process

Type 6

Outcome

Type 5 Pathophysiology

or disease process

Type 6 Outcome

Type 2 Target

occupancy

Type 3 Target

mechanis m

Type 4A Physiological

respons e

Type 4B Physiological

respons e

Type 0 Genotypephenotype

Type 1 Drug

concentration

Type 2 Target

occupancy

Type 3 Target

mechanis m

Type 4A Physiological

respons e

Type 4B

Physiological respons e

Type 0 /epytoneG

phenotype

Type 1 Drug

concentration

Quantitative relation between biomarkers

PoC PoP PoM PHC

Interspecies translational relation

Transduction to efficacy and/or Safety Target exposure Target engagement

Targetoccupancy

k

k

Target mechanism

Disease process

Outcome Patho-physiology

CpPlasma

Dose CeTarget site

k

PHARMACOKINETICS PHARMACODYNAMICS

Compound-specific properties System-specific properties

(a)

(b)

(c)


FIGURE I


Review

s�P

OSTSCREEN




alone, as a replacement, or in combination with another com-

pound. In addition, for novel target combinations, quantitative

understanding provides a way to understand the respective con-

tributions of multiple mechanisms of action at different targets,

which merge in the pathway (e.g., at the target engagement or

disease level, determining competition, addition or synergy of

effects [36]). For respiratory diseases, the challenges are quantifica-

tion of the target site distribution and limited availability of

preclinical models for chronic respiratory diseases, such as chronic

obstructive pulmonary disease. In the cardiovascular area, there is

an opportunity to learn about the cardiovascular system behavior

from both pharmacology and safety studies. For diabetes mellitus,

disease and biomarker models are relatively well developed, giving

the opportunity to build systematically in silico models to study

targets and combinations.

Implementation phaseThe development of quantitative pharmacology models with the

integration of preclinical and clinical information to support

decision-making requires a multidisciplinary approach and a close

interdependence between several disciplines, including pharma-

cology, drug metabolism and PK (DMPK), translational science,

safety, systems biology and pharmacometrics. Given that research

and development at AstraZeneca is organizationally divided into

therapeutic areas, cross-functional implementation teams for each

of these therapeutic areas were established to lead tailored imple-

mentation packages and activities based on the general principles

agreed in the global MBDDx workstream. The implementation

phase started with engagement with the line management of

senior global and therapeutic areas to obtain endorsement for

the implementation plans and to agree on expected outcomes.

The therapeutic area teams had an important role in the change

process and served as advocates for the MBDDx strategy, creating a

shared purpose within their different functions, facilitating scien-

tific support and awareness training to projects and funneling

questions and challenges back to the global team. This provided

an efficient way of identifying capability gaps across the organiza-

tion, and delivering more tailored awareness workshops and tech-

nical training courses. The awareness training for implementation

into projects was primarily performed via engagement in face-to-

face workshops, where the key skill groups, project leaders and

senior management were targeted. The concepts were illustrated

with examples from specific therapeutic areas, and project teams

were then engaged in workshops with the aim to obtain a health

check on MBDDx status, gaps and plans.

The generic drug project operating model and investment mile-

stone criteria have had the quantitative pharmacology aspects

embedded into them. Additionally, individual projects use a

translational science and biomarker strategy document to capture

the quantitative pharmacology aspects. This includes a status, gap

and risk analysis and resulting plan for biomarker development

and quantitative pharmacology analysis. This plan is developed

from the start of a project and is regularly updated before critical

investment decisions, such as lead optimization, candidate selec-

tion and during clinical development, and is used by local govern-

ance bodies in preparation for investment decisions. As a result of

these efforts, best practice has been established and preclinical

M&S scientists are contributing to projects in all phases. The M&S

scientist is responsible for defining the key M&S activities required

to increase the confidence in the project with respect to the

biological hypothesis and the human dose predictions, and to

deliver agreed activities to support quantitative elements of the

preclinical project translational strategy. This has been a consis-

tent success factor for the implementation across all therapeutic

areas; each M&S scientist is an advocate and works in partnership

with the DMPK scientist, pharmacologist and translational strate-

gist to apply the MBDDx strategy to drive the project forward.

Projects have generally embraced this approach and, with a strong

pull from governance, this has led to an increased expectation that

model-based approaches will be applied to all decisions.

Capability buildTo deliver the MBDDx strategy, an overall modeling capability

requires in-depth mathematical and computational skills as well as

specialism in the application of M&S to biology and disease. At the

start of this initiative, there was a clear capability and capacity gap

within AstraZeneca. To address this, dedicated preclinical M&S

groups were formed within each therapeutic area and were sized to

support the respective portfolio. Having therapy-aligned local

M&S groups has been a clear driver of success to establish strong

working relations, particularly with in vivo pharmacologists and

translational scientists. Alignment to the therapy areas has been

important overall for driving the effectiveness and impact of

MBDDx into projects. It was evident when forming these focused

M&S groups, that this resource and competence was scarce within

the company.

To address this resource gap, a four-pronged strategy was estab-

lished to build the internal capability. First, a concerted global

recruitment campaign was launched to attract talent from outside

AstraZeneca. Second, opportunities for accelerated development

were given to individuals in the organization that did not have the

formal training, or extensive experience in M&S, but had an

interest in, and demonstrated an aptitude for, M&S. More experi-

enced scientists were offered advanced training courses to increase

their overall technical skills in the application of advanced mod-

eling approaches. Third, seven collaborations were established

with key academic institutions to strengthen, in the longer term,

the pool of M&S scientists. Finally, ten internal scientific research

positions (post-doc) were created to provide an accelerated devel-

opment path for promising young PhD scientists as well as devel-

oping state-of-the-art expertise in translational biomarkers, novel

quantitative models and methodology.

All four elements were successful approaches in their own right,

albeit collectively, building a capability of this kind is difficult and

takes considerable time. For capacity increase, to build flexibility

in the total resource available and to access the technical skills

that might not be available internally, external capability provi-

sioned through partnerships with dedicated M&S providers was

established. To maintain this capability successfully in the med-

ium to long term, other factors such as succession planning,

competitive benefits, development opportunities via specializa-

tions and job rotations from other functions, will need to be

addressed. Moreover, empowering influential scientists in other

functions within the organization to become MBDDx ambassa-

dors can help advocating the importance and impact of this

approach.




Review

s�P

OSTSCREEN

Building a data platform to enable analysis of complexin vivo studiesEfficient use of modeling requires access to diverse and high-

quality data sets and a flexible data management infrastructure.

Achieving this task in large pharmaceutical companies is challen-

ging because the data sources and types differ among therapeutic

areas and a solution that fits one area might not be completely

transferable to another. Therefore, for preclinical in vivo data, a

single ontology, known as the in vivo data contract, was generated

from internal definitions and experimental procedures, with the

idea to link the biology and endpoints across studies and ther-

apeutic areas into common clusters. A high-level view of the

approach is described in Box 2. Establishing and maintaining

such a data contract had the benefit of enabling integration of

diverse data sets into a common platform that can be exploited at

the enterprise level; providing uniform retrieval of data and

BOX 2

Application of company-wide in vivo data reportingstandards: in vivo data contractThe in vivo data contract is a set of standards that creates a cross-therapeutic area framework for representing all in vivo data.Establishing and maintaining a data contract enables integration ofdifferent data sets (spanning different therapeutic areas) into theplatform through a common understanding of data types withoutthe need for detailed understanding of the experimentalprocedures, or recognition of common endpoints. This frameworkenables uniform retrieval of data and flexibility to manage theintegration of data. The timeline view (Figure I) represents a typicalin vivo study capturing the initiation of the model, dosing (acute or

Results view

NB: multiple measurements on a particular sample or animal are permitted

AnimalID = 123

1 – Body weight

2 – Plasma concentration

3 – Body weight

4 – Plasma concentration

5 – Body weight

6 – Brain concentration

7 – Biomarker concentration

Sample 1 (blood)

Sample 2 (blood)

Sample 3 (brain)

1 = 450 g

2 = 0.3 uM

3 = 448 g

4 = 0.4 uM

5 = 462 g

6 = 50 nmol/kg

7 = 190 nmol/kg

TIME

Dose (anaesthetic )

09:00 09:15

Preparation 1

09:30

Preparation 2

13:00

Induction 1

Induction 2

Measurement 1 (on animal – body weight )

Measurement 2 (on sample 1 – plasma concentration)

Sampling 1 Sample 1

(blood )

Timeline view

Dose 1

Setting up the study and the animal model (e.g., preparatory surger y, administration of challenge, e.g., to simulate a disease condition).

Preparation, induction, dose, measurement, sampling, te

The in vivo data contract

Measurement Result

FIGURE I


flexibility to manage data without imposing on other parts of a

complex work flow. Areas that required data to be expressed in a

rigid way (e.g., when specifying a dose route) and areas where

flexibility was essential to describe the in vivo experiment appro-

priately (e.g., when specifying the procedural details or unique

experimental details) were identified early. The implementation

of the solutions via a network of informaticians has resulted in the

streamlining of therapeutic area or in vivo model-specific pro-

cesses for the bench scientists, automating manual steps in pro-

cessing the data and significantly improving the quality of data

and metadata capture. Additional benefits of improving knowl-

edge sharing across multiple customer groups, with cross-links to

other data store systems, enables the rapid recovery of data to

support regulatory body inquires. Impact on user uptake and

engagement and the payback to the data generators were compel-

ling, as illustrated in Box 2.

chronic) and different measurements or analyses that might bemade from samples taken in the course of the experiment. Thetimeline is not fixed; new measurements and sampling procedurescan be added while the experiment is in progress. The results view(Figure I) exemplifies how the study is structured to representdifferent data points on each individual animal that contribute tothe whole study. Analysis performed long after the termination ofthe experiment can be added into the data set (not shown). Thebenefits of implementing a single data platform are represented asdirect resource savings delivered through addressing datahandling and storage.

Measurement 5 (on animal – body weight)

14:00 15:0 0

Measurement 3 (on animal – bod y weight)

Measurement 4 (on sample 2 – plasma concentration)

Measurement 6 (on sample 3 – brain concentration)

Measurement 7 (on sample 3 – biomarker concentration)

Terminatio n

Sampling 2 Sample 2

(blood) Sampling 3 Sample 3 (brain)

Dose 2

Working with tailored templates or tools for data handling by pharmacology and DMPK scientists to reduce manual data manipulation

40 60% of data analysis time and/or study type

Addressing the automation of integration of PK, biomarkers and covariates (e.g., bodyweight) to generate standard plots

0.5 FTE across 5 8 drug projects

Automating the flow of information and data between bioscience and DMPK improves accuracy of data transfer

Up to 0.8 FTE per project

Exposing to data through a single platform 0.4 FTE per modeler

Resource savings of a single data platform

rmination etc. are all formally defined terms in the data contract





The main investment and challenge of this task were in the

infrastructure and data load, whereas the functionality that

retrieves individual animal data to enable modeling of compound

exposure, inhibition of primary or target related biomarkers, effi-

cacy and therapeutic index has been the minor task of this under-

taking. The overall impact was a significant 40% saving of the

preclinical modeler’s time, therefore increasing the impact of

modeling through drug projects in AstraZeneca.

To ensure a continuum from MBDDx to MBDD and that the

right learning is taken back from the clinic (back-translation),

integration of preclinical and clinical data is essential. To under-

stand the infrastructure requirements for forward- and back-

translation of emerging clinical data, an additional investment

was made to pilot the integration of various clinical and pre-

clinical data sources to form a project-centric database. This

database was exploited to facilitate projects to define and

respond to key questions that are addressed in early clinical

studies. These key questions concerned target engagement,

safety margin and/or therapeutic windows, impact of different

dose schedules, forward- and back-translation of efficacy and

safety biomarkers, and benchmarking (comparator) data. In this

pilot, such knowledge was extracted and visualized using an

internally developed knowledge management tool. The ‘Knowl-

edge plot’ enabled full integration of preclinical and clinical,

efficacy and safety data and increased the flexibility in visualizing

information during all development phases. This pilot clearly

demonstrated that real-time forward- and back-translation had

significant impact on decisions made to project teams, by

improving cross-functional work and increasing transparency

of the large amount of compound and project information. This

initiative also highlighted the requirements on data and infra-

structure for aligning several existing translational data plat-

forms across preclinical and clinical. It also exposed the need

to develop a more enterprise solution compared with those used

in the pilot projects, which were tailored to the needs of the

individual project.

BOX 3

Can an intermittent, rather than continuous, doseschedule offer improved tolerability while maintainingefficacy?The AKT signaling pathway is one of the most frequentlyderegulated pathways in human cancer. The AKT protein is animportant node in this signaling cascade that controls cell survivaland progression through the cell cycle. Hyperactivation of AKT leadsto deregulation of this complex signaling cascade and thisunbalance is implicated in tumorigenesis. AKT inhibitors are knownto act by preventing cell proliferation and, at high concentrations,cell apoptosis. The measurement of down-stream kinases can beused as predictors of the effects of AKT inhibition [44]. To elucidatethe schedule dependence of antitumor efficacy in a mouse breastcancer model (BT474c xenografted cell-line), a PKPD-efficacy modelwas established to relate the PK of an Akt inhibitor with the resultingbiomarker dynamics (PD) on the AKT signaling pathway, specificallypS6 as a measure of cell proliferation and uGSK3b as a measure ofapoptotic threshold. The PD response was then used to drive cellinhibition and cell death on a modified model of tumor growth [45].

Project impactTo demonstrate the return on investment made by the business,

the impact of adopting the MBDDx strategy into projects across

the therapeutic areas was captured via a quarterly collection of case

study examples. These case studies illustrate how MBDDx has

helped projects avoid, or reduce, cost and increase the likelihood

of technical success. These examples have been powerful in raising

general awareness and demonstrating the value of applying the

quantitative pharmacology strategy. Three examples are illu-

strated in more detail in Boxes 3–5.

The financial investment and, thus, potential cost savings or

cost avoidance during early discovery phases might seem relatively

small compared with the clinical stages, and these are usually

measured only to the next discovery or development phase.

However, the average preclinical costs for a successful launch is

US$270 million [1] and, therefore, ensuring a higher success rate

via early termination or reduced cycle time by doing the right

experiments in a more optimal way, will deliver return on the

capability and infrastructure investments. Also, by having a better

quantitative understanding of the biological hypothesis, better

quality candidates will be taken forward with more informed

decisions on predicted dose and scheduling for clinical testing,

and this will offer large cost avoidance and savings over a longer

time frame. For example, it took 6 years of Phase 1 development of

flavopiridol by the US National Cancer Institute to come to a

recommended schedule, because it takes 1–2 years and 50–100

patients to get sufficient data to rule out or in a schedule. If

preclinical insight and testing could reduce the number of permu-

tations, this can lead to both a substantive cost ($10 million per

tested schedule) and time (1 year) savings during early clinical

development. Such a preclinical approach is illustrated in Box 3 by

establishing a robust view on dose and schedule options for

clinical studies.

Efficiency improvements in screening cascades and reduced

cycle times for make-test cycles have been demonstrated through

establishing in vitro–in vivo correlations (see also [37]) and devel-

By having a quantitative understanding of these biomarkers, whichrepresent a measure of phenotypic responses that result in efficacy(tumor growth inhibition), the model enabled hypothesis-driveninterrogation of the biology and the balance of contribution tooverall efficacy from multiple mechanisms of drug action.Understanding these relationships is useful when consideringdifferent scheduling options, for instance to optimize a therapeutic–tolerability margin. The complex interplay between drug exposure,target inhibition, phenotypic responses and efficacy would require aconsiderable number of experimental studies to find optimal doseschedule options using empirical study design strategies. Bycontrast, a model of this kind offers the opportunity to simulatedifferent scheduling options and direct the experimental design to afew studies to validate the hypothesis. The additional detailencapsulated within a PKPD model provides greater confidence inextrapolating predictions to human, both in predictions beforethe clinic and in simulations of alternative schedules in responseto the emerging tolerability profile from Phase 1 patient studies(Figure III).




Reduced proliferation rate

Low apoptotic threshold = cell death

Tumour regression

pS6 uGSK3β

Reduced proliferation rate

Low apoptotic threshold = cell death

Growth inhibition

pS6 uGSK3β

High

Low

Co

nce

ntr

atio

n

Cycling cells

PK pGSK3β

pS6

Cell death

Time (h)

1.2

1

0.8

0.6

0.4

0.2

0300

Control 150 mg/kg BD 300 mg/kg QD (4days)

500 700

Apoptosis

Anti- proliferation

Incr

ease

d

apo

pto

sis

Red

uce

d

pro

lifer

atio

n

(a)

(b)

(c) Simulated efficacy Simulated biomarkers

Continuous dosing 4 days per week 2 days per week

Biomarkers Efficacy

Tu

mo

ur

volu

me

(cm

3 )


150

% o

f con

trol

Time in hours

Time in hours

Time in hours

pGSK3b pS6

100

50

072 84

1001

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2300 400 500 600 700

80

60

40

20

050 100 150

pGS

K3b

%C

ontr

ol

100

80

60

40

20

050 100 150

pS6

%C

ontr

ol

96 108 120

FIGURE I

example of modeling and simulation in oncology: defining efficacious schedules. (a) Schematic of the pharmacokinetic–pharmacodynamic (PKPD) efficacy model

where two biomarkers are used to drive two effects in the tumor growth model. (b) The model was parameterized using experimental PD (left panel) and tumor

growth data (right panel). (c) The model was simulated to identify the equi-efficacious regimen to define clinical dose scheduling options to balance efficacy andtolerability.

Review

s�P

OSTSCREEN

opment of calculation tools. An example of improved efficiency

and improved decision-making is illustrated in Box 4, where a

minimalistic single time-point safety PD study could be used to

predict a longer-term preclinical safety endpoint. The estimated in

vivo potencies in the safety and efficacy disease model enabled

quantification of the safety window, which facilitated the govern-

ance decision to invest in the lead optimizing phase. The disease

model efficacy also correlated well with in vitro potency, enabling

a priori estimate of the level of separation between efficacy and


safety based on a relatively cheap and quick turnaround screening

cascade.

Other examples have demonstrated that, in the case of good

quantitative understanding of the relation between in vitro and in

vivo potency, characterization and optimization could be done

primarily on in vitro data, whereas only the most promising

candidate would be tested in vivo to confirm the prediction. This

has led to reduced need for costly and time-consuming in vivo

experimentation and more rapid progression of the project

Drug Discovery Today � Volume 00, Number 00 � July 2013


BOX 4

Lead optimization investment decision based onquantitative understanding of efficacy and safety withindiabetes mellitus and diabetic complicationsIt is known from clinical studies that antagonism of a particulartarget can delay disease progression, but that it is associated withan undesired effect, limiting the use of currently availableantagonists. The project team aimed to develop a compound withminimal undesired effect while retaining efficacy. Safety systemparameters were derived by applying a pool precursor model todescribe an acute, short term safety pharmacological study with atime-resolved Type 3 biomarker responding to varyingprovocation routines of an endogenous agonist in the presence ofa competing antagonist (compound A). This model was then usedto estimate in vivo safety biomarker inhibitory concentration 50(IC50) for compounds A–F, by incorporating their PK profiles andusing a minimalistic single PD time point sampling study (FigureIa). The IC50s derived in this manner were devoid of confoundingPK or study design differences and correlated well on a rankinglevel to safety Type 4 biomarkers in longer term studies (data notshown). In vivo efficacy disease progression biomarker (Type 5)

data were then modeled by a linear disease progression modeland in vivo efficacy IC50s were obtained for compounds A–F(Figure Ib). In vivo efficacy IC50s were also well correlated to in vitroIC50s (not shown). The ratio of in vivo safety to efficacy IC50 wasused as a quantitative measure of separation between efficacyand safety (Figure Ic). The resulting varying range of ratiossupports the hypothesis that separation between target-mediatedefficacy and safety is possible (compounds D and F). Interspeciestranslatability of the preclinical disease progression model wasconfirmed by measured clinical and preclinical efficacy of a clinicaltool compound using the same biomarker in all species andcomparing this to the predicted daily receptor occupancy, basedon in vitro IC50 data (Figure Id). This example demonstrates thatapplying a MBDDx approach gave quantitative evidence thatseparation between efficacy and safety could be obtained for thistarget, and suggests interspecies translatability of efficacy. Thesetwo factors were key determinants for the lead optimizationdecisions and for driving this project forward. The results alsoinformed subsequent in vivo study designs to ensure that thesewere quantitative and conclusive.

0

0.8

0 2 4

0.8

2 4 6

0

0.8

0 2 4 6

2 4

0

0.8

0 2 4 6

Preclinical difference between efficacy(Type 5BM) and safety (Type 3BM) IC50 s

Compound Separation: Safety / Efficacy in vivo IC50

A clinical tool

1.2

B 0.4

C 0.3

D 9

E 0.3

F 20

Preclinical and clinical efficacywith clinical tool

0

20

40

60

80

100

0 20 40 60 80 100

Res

pons

e

(% fo

ld o

f pla

cebo

/veh

icle

)

Predicted daily receptor occupancy (%)from in vitro IC50

Human

Mouse

Rat

kout

ka

kin Responserate

kout

Preclinical safety PKPD model

PD Pool

Vehicle injection

Drugconc

Cl

kout

System Drug function Competitive interaction

PK

Dose

Compound: antagonist

Cp

Dose ka

ktol

Endogenous Agonist:

m ktol +

kin -

Example of model fit for PD using fixed system parameters

Pool/precursor PKPD model with competitive interaction

Res

po

nse

Time (h) O Experimental values

Model fit

Vehicle

Antagonist dose

1 mg/kg Safetyin vivo IC50

(Type 3BM) Progression

rate PD (Type 5)

Dose Drugconc

Cl ka

PK

Linear disease progression PKPD model

-

Drug function: Imax- model

PD

Main assumption: -antagonist can fully block the disease progression (demonstrated by a compound)

Example of model fit for PD

EfficacyIn vivo IC50

(Type 5 BM)

Preclinical efficacy PKPD model (a) (b)

(c) (d)

0.05

5

0 200 400 600 800

Dis

ease

Bio

mar

ker

Time (hrs)

O Vehicle O Treatment

Model fit Model fit

0.1 mg/kg

10 mg/kg


FIGURE I


[37,38]. Box 5 provides an example of an early termination of a

lead series by applying quantitative modeling, which enabled

governance and the project team to come to a rational and

informed decision. Other significant efficiency improvements

have been demonstrated by the design and implementation of

software libraries to support routine model simulations, parameter

estimations, reading and writing PK and PD data from/to Excel,

and writing presentations to PowerPoint, as well as automated

human dose predictions. With data platforms that expose data on

an animal level and experiments that are more sophisticated and




BOX 5

Early termination of project via integration of in vitro, invivo efficacy and safetyA bacterial dynamics model was developed to describe the killingkinetics of selected compounds against Pseudomonas aeruginosa.Using a population modeling approach, the model was fittedsimultaneously to the in vitro time-kill results obtained at sevenconcentration levels, ranging from 0 to 16-fold [the minimuminhibitory concentration (MIC)]. The model and its parameterestimates were then linked to the mouse lung PK model developedfor the same compound, and the combined PKPD model was usedto predict the in vivo time course of response in a immune-compromised mouse lung model. The population modelingapproach described the in vitro data well and was also able topredict the in vivo data, hence confirming the PKPD in vitro–in vivocorrelation (IVIVC, Figure Ia). Simulations of in vivo response werethen conducted for multiple dose and regimens (Figure Ib). Thedosing regimen required to achieve stasis (no net bacterial growthfrom baseline) at 24 h in the immune-compromised mouse modelwas determined to be 700 mg/kg every 2 h for 4 doses (a total dose

of 2800 mg/kg/day). This high and frequent dosing regimen wasdeemed not achievable based on the mouse tolerability data andthe physicochemical properties of the compound. Through M&S, itwas demonstrated that (i) the in vitro time-kill data can be utilized topredict the in vivo time course of the selected compound in a mouseinfection model; and ii) high doses would be required to achieve invivo efficacy, which were not feasible from a formulation perspective.In addition, the results demonstrated that the exposures required forefficacy were exceeding concentrations in a safety study thatdemonstrated adverse effects. The results, together with the mousetolerability and physicochemical data, suggested difficulty inprogressing with this chemical series for the program. This exampledemonstrates the potential utilities of modeling the in vitro time-killdata in infection and establishing the PKPD IVIVC early on forinfection programs. The prediction of efficacious dose and feasibilityof dosing avoided the use of resources and animals. The samemodeling approach could be applied to other compounds and seriesto predict in vivo efficacy based on in vitro data before having any invivo efficacy data.

4x MIC

2x MIC

Stasis

1 log kil l

(a)

(b)

10

2.5

2.0

1.5

1.0

0.5

0.0

5.5

4.5

3.5

2.5

0 8 16 24 0 8 16 24 0 8 16 24 0 8 16 24

Observations

50 mg/kg/day

2800 mg/kg/day 3200 mg/kg/day 3600 mg/kg/day 4000 mg/kg/day

100 mg/kg/day 150 mg/kg/day

Simulations

8

6

4

2

10

8

6

4

2

0 8 16 24 0 8 16 24 0 8 16 240 8

Time (Hours) Time (Hours)

Time (Hours)

Bact

eri

al l

oad (

log10(C

FU

/mL))

Fre

e lu

ng c

onc.

(m

g/L

)

Bact

eri

al l

oad (

log10(C

FU

/lung))

Bact

eri

al l

oad (

log10(C

FU

/lung))

16 24


0x MIC0.5x MIC1x MIC

8x MIC

2x MIC4x MIC X

16x MIC

FIGURE I

A bacterial dynamics model to describe the killing kinetics of selected compound against Pseudomonas aeruginosa. (a) In vitro–in vivo correlation: a population

modeling approach was used to describe the in vitro bacteria kill, which was then linked to a pharmacokinetic (PK) model for lung exposure and used to describethe in vivo response of different doses in a mouse infection model. Symbols are the observations and lines are model predictions for in vitro time-kill (left panel)

and in vivo time course data (right panel). (b) Simulated lung PK profile (top panel) and bacterial effect (lower panel) for 700 mg/kg to 1000 mg/kg dosed every 2 h

for 4 doses. The dosing regimen required to achieve stasis (no net bacterial growth from baseline) at 24 h in the immune-compromised mouse model wasdetermined to be 700 mg/kg every 2 h for 4 doses (or 2800 mg/kg/day).


Review

s�P

OSTSCREEN




well designed, there were numerous of applications of advanced

modeling techniques, such as population methods and meta-

analyses, to explore fully understanding of the system properties

in animal and humans and the variability used for extrapolation

purposes [39–43].

Concluding remarksOver the past couple of years, AstraZeneca has moved from a

situation where preclinical quantitative approaches were applied

consistently in very few projects, to a situation where it is con-

sistently deployed across most projects in the portfolio, accom-

panied by strong support from governance bodies. This has

significantly impacted the confidence in decision-making based

upon MBDDx knowledge for early termination of compounds,

more efficient and focused lead optimization programs, and quick

and efficient progression of the most promising candidates. Key

factors for MBDDx success have been the local integration of M&S

to address the specific therapy area needs and challenges, which

also helped define shared purpose and goals for the constituent

functions of MBDDx, namely pharmacology, translational

science, safety and DMPK. Further success factors have been

the desire from project teams, governance and senior manage-

ment to utilize MBDDx in decision-making. In addition, the

development of a company-wide preclinical information plat-

form has been a pivotal factor in success. Important next steps are

the further integration with computational biology and clinical

pharmacometrics (i.e. quantitative and systems pharmacology

[47]) building towards a more efficient modeling continuum to

drive effective business decisions.

AcknowledgementsThe authors like to acknowledge the late Thierry Groblewski, and

thank many other colleagues with current or previous affiliation

with AstraZeneca for their invaluable contributions to the MBDDx

workstream activities. These include Corinne Reimer, John

Clapham, Pete Webborn, Ulf Eriksson, Gemma Satterthwaite,

Corinna Fletcher, James Hinchcliffe, Scott Thomas, Tim Piser,

Johan Gabrielsson and Hugues Dolgos.

References

1 Paul, S.M. et al. (2010) How to improve R&D productivity: the pharmaceutical

industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–214

2 Kola, I. and Landis, J. (2004) Can the pharmaceutical industry reduce attrition rates?

Nat. Rev. Drug Discov. 3, 711–715

3 Manolis, E. et al. (2013) The role of modelling and simulation in development and

registration of medicinal products: output from the EFPIA/EMA modelling and

simulation workshop. CPT Pharmacomet. Syst. Pharmacol. 2, e31

4 Arrowsmith, J. (2011) Trial watch: phase II failures: 2008–2010. Nat. Rev. Drug

Discov. 10, 328–329

5 Arrowsmith, J. (2011) Trial watch: phase III and submission failures: 2007–2010.

Nat. Rev. Drug Discov. 10, 87

6 Morgan, P. et al. (2012) Can the flow of medicines be improved? Fundamental

pharmacokinetic and pharmacological principles toward improving Phase II

survival. Drug Discov. Today 17, 419–424

7 Zhang, L. et al. (2006) Model-based drug development: the road to quantitative

pharmacology. J. Pharmacokinet. Pharmacodyn. 33, 369–393

8 Krishna, R. et al. (2007) Effective integration of systems biology, biomarkers,

biosimulation, and modeling in streamlining drug development. J. Clin. Pharmacol.

47, 738–743

9 Zhang, L. et al. (2008) Concepts and challenges in quantitative pharmacology and

model-based drug development. AAPS J. 10, 552–559

10 Zhang, L. et al. (2010) Fostering culture and optimizing organizational structure for

implementing model-based drug development. J. Clin. Pharmacol. 50, 146S–150S

11 Stone, J.A. et al. (2010) Model-based drug development survey finds

pharmacometrics impacting decision making in the pharmaceutical industry. J.

Clin. Pharmacol. 50, 20S–30S

12 Wetherington, J.D. et al. (2010) Model-based drug development: strengths,

weaknesses, opportunities, and threats for broad application of pharmacometrics in

drug development. J. Clin. Pharmacol. 50, 31S–46S

13 Agoram, B.M. et al. (2007) The role of mechanism-based pharmacokinetic–

pharmacodynamic (PK-PD) modelling in translational research of biologics. Drug

Discov. Today 12, 1018–1024

14 Lowe, P.J. et al. (2007) On the anticipation of the human dose in first-in-man trials

from preclinical and prior clinical information in early drug development.

Xenobiotica 37, 1331–1354

15 Van Der Graaf, P.H. and Gabrielsson, J. (2009) Pharmacokinetic–pharmacodynamic

reasoning in drug discovery and early development. Future Med. Chem. 1, 1371–1374

16 Gobburu, J.V. and Lesko, L.J. (2009) Quantitative disease, drug, and trial models.

Annu. Rev. Pharmacol. Toxicol. 49, 291–301

17 Peck, C.C. (2010) Quantitative clinical pharmacology is transforming drug

regulation. J. Pharmacokinet. Pharmacodyn. 37, 617–628

18 Manolis, E. and Herold, R. (2011) Pharmacometrics for regulatory decision making:

status and perspective. Clin. Pharmacokinet. 50, 625–626

19 Visser, S.A. et al. (2013) Modeling and simulation at the interface of non-clinical and

early clinical drug development. CPT Pharmacomet. Syst. Pharmacol. 2, e30

20 Agoram, B.M. and Demin, O. (2011) Integration not isolation: arguing the case for

quantitative and systems pharmacology in drug discovery and development. Drug

Discov. Today 16, 1031–1036

21 Yu, J. et al. (2011) From target selection to the minimum acceptable biological effect

level for human study: use of mechanism-based PK/PD modeling to design safe and

efficacious biologics. AAPS J. 13, 169–178

22 Gabrielsson, J. et al. (2009) Early integration of pharmacokinetic and dynamic

reasoning is essential for optimal development of lead compounds: strategic

considerations. Drug Discov. Today 14, 358–372

23 Danhof, M. et al. (2007) Mechanism-based pharmacokinetic–pharmacodynamic

modeling for the prediction of in vivo drug concentration–effect relationships,

Application in drug candidate selection and lead optimization. In Comprehensive

Medicinal Chemistry, (Vol II) (Taylor, J.B. and Triggle, D.J., eds) pp. 885–908,

Elsevier

24 Mager, D.E. et al. (2009) Scaling pharmacodynamics from in vitro and preclinical

animal studies to humans. Drug Metab. Pharmacokinet. 24, 16–24


modeling: biophase distribution, receptor theory, and dynamical systems analysis.

Annu. Rev. Pharmacol. Toxicol. 47, 357–400

26 Van der Graaf, P.H. and Benson, N. (2011) Systems pharmacology: bridging systems

biology and pharmacokinetics–pharmacodynamics (PKPD) in drug discovery and

development. Pharm. Res. 28, 1460–1464


modeling – a new classification of biomarkers. Pharm. Res. 22, 1432–1437

28 Peck, R.W. (2007) Driving earlier clinical attrition: if you want to find the needle,

burn down the haystack. Considerations for biomarker development. Drug Discov.

Today 12, 289–294

29 Agoram, B.M. and Van der Graaf, P.H. (2012) Biomarkers and biomeasures: key

enablers for pharmacokinetic–pharmacodynamic modeling in drug discovery and

development. Bioanalysis 4, 1143–1145

30 Sheiner, L.B. (1997) Learning versus confirming in clinical drug development. Clin.

Pharmacol. Ther. 61, 275–291

31 Chien, J.Y. et al. (2005) Pharmacokinetics/pharmacodynamics and the stages of

drug development: role of modeling and simulation. AAPS J. 7, E544–E559

32 Gabrielsson, J. and Green, A.R. (2009) Quantitative pharmacology or

pharmacokinetic pharmacodynamic integration should be a vital component in

integrative pharmacology. J. Pharmacol. Exp. Ther. 331, 767–774

33 Gabrielsson, J. et al. (2010) Optimising in vivo pharmacology studies: practical

PKPD considerations. J. Pharmacol. Toxicol. Methods 61, 146–156

34 Gabrielsson, J. et al. (2011) Pharmacodynamic–pharmacokinetic integration as a

guide to medicinal chemistry. Curr. Top. Med. Chem. 11, 404–418


http://refhub.elsevier.com/S1359-6446(13)00158-X/sbref0005


























































































Review

s�P

OSTSCREEN

35 Bueters, T.J. et al. (2009) Correct assessment of new compounds using in vivo

screening models can reduce false positives. Drug Discov. Today 14, 89–94

36 Jonker, D.M. et al. (2005) Towards a mechanism-based analysis of

pharmacodynamic drug–drug interactions in vivo. Pharmacol Ther. 106, 1–18

37 Kalezic, I. et al. (2013) In vivo and ex vivo inhibition of spinal nerve ligation-

induced ectopic activity by sodium channel blockers correlate to in vitro

inhibition of NaV1.7 and clinical efficacy: a pharmacokinetic–pharmacodynamic

translational approach. Pharm. Res. http://www.dx.doi.org/10.1007/s11095-013-

0979-6

38 Janson, J. et al. (2012) Population PKPD modeling of BACE1 inhibitors induced

reduction in brain Ab levels in vivo. PAGE 21 Abstr. 2340

39 Ekerot, E. et al. (2011) Mechanism-based pharmacokinetic–pharmacodynamic

feedback model of thyroid hormones after inhibition of thyroperoxidase in the dog.

Cross-species prediction of thyroid hormone profiles in rats and humans. PAGE 20

Abstr. 2150

40 Niva, C. et al. (2013) Has inhibition of Ab production adequately been tested as

therapeutic approach in mild AD? A model-based meta-analysis of g-secretase

inhibitor data. Eur. J. Clin. Pharm. http://dx.doi.org/10.1007/s00228-012-1459-3


41 Van Maanen, E. et al. (2012) Investigation of a data transformation procedure to

improve identifiability of efficacy and potency: application to continuous elevated

plus-maze data in rats. PAGE 21 Abstr. 2436

42 Bergman, K. et al. (2012) Quantification of antidepressant and sedative effect of two

NMDA channel blockers, AZD6765 and ketamine, in an animal model of depression

using count data. PAGE 21 Abstr. 2431

43 Wallsten, C. et al. (2012) Quantification of the drug effect and exploration of

mechanism of action of two NMDA channel blockers, AZD6765 and Ketamine,

using mouse EEG data. PAGE 21 Abstr 2375

44 Davies, B.R. et al. (2012) Preclinical pharmacology of AZD5363, an inhibitor of AKT:

pharmacodynamics, antitumor activity, and correlation of monotherapy activity

with genetic background. Mol Cancer Ther. 11, 873–887

45 Simeoni, M. et al. (2004) Predictive pharmacokinetic–pharmacodynamic modeling

of tumor growth kinetics in xenograft models after administration of anticancer

agents. Cancer Res. 64, 1094–1101

47 Sorger, P.K. et al. (2011) Quantitative and systems pharmacology in the post-

genomic era: new approaches to discovering drugs and understanding therapeutic

mechanisms. NIH White Paper by the QSP Workshop Group





http://www.dx.doi.org/10.1007/s11095-013-0979-6

http://www.dx.doi.org/10.1007/s11095-013-0979-6







http://dx.doi.org/



















Drug Discovery Today Volume Issue 2013 [Doi 10.1016_j.drudis.2013.05.012] Visser, Sandra a.G.;...

Documents

Transcript of Drug Discovery Today Volume Issue 2013 [Doi 10.1016_j.drudis.2013.05.012] Visser, Sandra a.G.;...