Pierre Gressens

Modèles animaux :

Intérêts et limites

• Neuroprotective strategies as an example

• False positive studies : what should we learn from

them ?

• True negative studies : why are they important ?

• False negative studies : what do they tell us ?

Focus & plan

• Adult stroke field : huge failure in clinical trials with drugs

protective in animal models (except for tPA)

False positive studies

• Adult stroke field : huge failure in clinical trials with drugs

protective in animal models (except for tPA)

• Pessimistic interpretation : animal models not predictive of

humans

False positive studies

• Adult stroke field : huge failure in clinical trials with drugs

protective in animal models (except for tPA)

• Pessimistic interpretation : animal models not predictive of

humans

• Scientific approach : why ?

False positive studies

• Animal studies

- “wrong” compound : PK, PD, BD, BBB, …, wrong target, …

False positive studies

• Animal studies

- “wrong” compound : PK, PD, BD, BBB, …, wrong target, …

- “wrong” design : blinded, randomized, stats, controls (KOs,

behavior)

False positive studies

• Animal studies

- “wrong” compound : PK, PD, BD, BBB, …, wrong target, …

- “wrong” design : blinded, randomized, stats, controls (KOs,

behavior)

- confounding variables

False positive studies

• Animal studies

- “wrong” compound : PK, PD, BD, BBB, …, wrong target, …

- “wrong” design : blinded, randomized, stats, controls (KOs,

behavior)

- confounding variables

- T°

- time of the day, season, …

- sex

- maternal stress, maternal care, maternal feeding, …

- person performing model, tests, analyses, …

False positive studies

Temperature

Thoresen et al., unpublished data

0

1

2

3

4

Mean

Glo

bal P

ath

olo

gy S

core

Control 32°C 37°C 38°C 39°C

Post-HI Recovery Temperature

3

Time of the day

Bednarek & Gressens, unpublished data

Maternal stress

Rangon et al., J Neurosci 2007

The ALS lesson

Scott et al., ALS 2008

SOD1 mutant = ALS modelRiluzole protection(increased lifespan)

The ALS lesson

Scott et al., ALS 2008

SOD1 mutant = ALS modelRiluzole protection(increased lifespan)

5429 miceRiluzole efficacy

computer analysis

The ALS lesson

Scott et al., ALS 2008

SOD1 mutant = ALS modelRiluzole protection(increased lifespan)

5429 miceRiluzole efficacy

computer analysis

confounding biological factors

optimal study design

The ALS lesson

Scott et al., ALS 2008

SOD1 mutant = ALS modelRiluzole protection(increased lifespan)

5429 miceRiluzole efficacy

computer analysis

confounding biological factors

optimal study design

optimal study design

8 « protective » drugswell-powered study

The ALS lesson

Scott et al., ALS 2008

SOD1 mutant = ALS modelRiluzole protection(increased lifespan)

5429 miceRiluzole efficacy

computer analysis

confounding biological factors

optimal study design

optimal study design

8 « protective » drugswell-powered study

no effect on lifespan !!!

The ALS lesson

Scott et al., ALS 2008

SOD1 mutant = ALS modelRiluzole protection(increased lifespan)

5429 miceRiluzole efficacy

computer analysis

confounding biological factors

optimal study design

optimal study design

8 « protective » drugswell-powered study

no effect on lifespan !!!

? previous studies = biased

• Animal studies

- “wrong” compound : PK, PD, BD, BBB, …, wrong target, …

- “wrong” design : blinded, randomized, stats, confounding

variables

- healthy vs sick animals

False positive studies

Impact of systemic inflammation on neuroprotection

Gressens et al., Eur J Pharm 2008Gressens et al., unpublished

Impact of systemic inflammation on neuroprotection

Gressens et al., Eur J Pharm 2008Gressens et al., unpublished data

Impact of systemic inflammation on neuroprotection

Gressens et al., Eur J Pharm 2008Gressens et al., unpublished data

Impact of systemic inflammation on neuroprotection

Gressens et al., Eur J Pharm 2008Gressens et al., unpublished data

• Animal studies

- “wrong” compound : PK, PD, BD, BBB, …, wrong target, …

- “wrong” design : blinded, randomized, confounding variables

- healthy vs sick animals

• Human clinical trials

- too “stringent” outcome

- death vs survival of impaired patients

False positive studies

The catch 22

0

Damage

Death

Insult

Neuroprotection

Protective effect on mortality?

• allow to rule out potential pathways and targets

True negative studies

• allow to rule out potential pathways and targets

… if studies correctly performed !

• good rationale (hypothesis to test)

• good design :

- sufficient power !!!

- multiple models

- multiple species

True negative studies

NADPH oxidase

• oxidative stress is deleterious for the brain

• inhibition of NADPH oxidase = neuroprotective in

adults

• ? good target in neonates

NADPH oxidase: not a good target in neonates

Doverhag et al., NBD 2008

NADPH oxidase: not a good target in neonates

Doverhag et al., NBD 2008

• what do they tell us ?

False negative studies

• what do they tell us ?

• different case scenarios …

False negative studies

• power calculation taking into account

- variability of procedure

- variability of outcome variable

Methodological biases

Power

(n=8/group)

Power

p = 0.0764(n=8/group)

Power

p = 0.0764(n=8/group) (n=16/group)

Power

p = 0.0764(n=8/group)

p = 0.0088(n=16/group)

• power calculation taking into account

- variability of procedure

- variability of outcome variable

• appropriate outcome & readout, combined R/

Methodological biases

Cx Hipp Cer Bs.g Thal0

4

3

2

1

Bra

in p

atho

logy

sco

reHypothermia + drug

Haland et al., Pediat Res 1997

Cx Hipp Cer Bs.g Thal0

4

3

2

1

Bra

in p

atho

logy

sco

reHypothermia + drug

Haland et al., Pediat Res 1997

- optimized HT- drug effect ? (complex paradigms & analyses or -)

Cx Hipp Cer Bs.g Thal0

4

3

2

1

Bra

in p

atho

logy

sco

reHypothermia + drug

Haland et al., Pediat Res 1997

- optimized HT- drug effect ? (complex paradigms & analyses or -)

- « human efficacy » HT- effect of drug on a cooled brain

• power calculation taking into account

- variability of procedure

- variability of outcome variable

• appropriate outcome & readout, combined R/

• dose-response curve

Methodological biases

Dose-response : U-shape curve

Sokolowska et al., submitted

• power calculation taking into account

- variability of procedure

- variability of outcome variable

• appropriate outcome & readout, combined R/

• dose-response curve

• BD (BBB penetration, degradation, …), PK, species

specificities

Methodological biases

Administration schedule

Gressens et al., unpublished data

Administration schedule

Gressens et al., unpublished data

• pre-clinical drug testing ≠ search for targets

Mixed effects

• pre-clinical drug testing ≠ search for targets

• cell type : neurons vs microglia / astroglia

=> cell type-specific conditional KOs

Mixed effects

• pre-clinical drug testing ≠ search for targets

• cell type : neurons vs microglia / astroglia

=> cell type-specific conditional KOs

• timing issue : early M1 microglia vs late M2 microglia

=> time-course of lesions

Mixed effects

M1 & M2 microglia

Kigerl et al., J Neurosci 2009

M1 & M2 microglia

Kigerl et al., J Neurosci 2009

• pre-clinical drug testing ≠ search for targets

• cell type : neurons vs microglia / astroglia

=> cell type-specific conditional KOs

• timing issue : early M1 microglia vs late M2 microglia

=> time-course of lesions

• responders vs non-responders

Mixed effects

• ! p>0.05 ≠ groups are similar

= groups are not statistically different

Responders & non-responders

• ! p>0.05 ≠ groups are similar

= groups are not statistically different

Responders & non-responders

p = 0.7182

• ! p>0.05 ≠ groups are similar

= groups are not statistically different

Responders & non-responders

p = 0.7182

• ! p>0.05 ≠ groups are similar

= groups are not statistically different

Responders & non-responders

p = 0.7182

• experimental bias

• maternal care bias

• other bias

Responders & non-responders

• experimental bias

• maternal care bias

• other bias

• ? mimics some human situation

Responders & non-responders

• experimental bias

• maternal care bias

• other bias

• ? mimics some human situation

• ? mechanism : epigenetics

Responders & non-responders

• experimental bias

• maternal care bias

• other bias

• ? mimics some human situation

• ? mechanism : epigenetics

• ad hoc statistical tools to confirm R vs non-R

• mechanistic approaches

Responders & non-responders

Acknowledgements

Vincent DegosAngela KaindlCatherine VerneyVincent El GhouzziStéphane PeineauStéohanie SigautAnne-Marie BodiouValérie BiranPascal DourneauSophie LebonLeslie SchwendimannTiffen Le Charpentier

Olivier BaudRomain FontaineJérémie Dalous

Cobi HeijnenAnnemieke KavelaarsCora Nijboer

Elie SalibaGéraldine Favrais

Petra HuppiStéphane Sizonenko

Yvan van den Loojj

Bernard Thébaud

Ulrika AdenMax Winerdal

Jon Lampa

Ursula Felderhoff-MueserMatthias Keller

Olaf DammannChristiane Dammann

Wolfgang Bueter

Henrik HagbergDavid EdwardsDenis AzzopardiMary Rutherford

Catie RoussetEtienne Jacotot

Michael SpeddingPhilippe Delagrange

Esther Shenker

Shyamala ManiParthiv Haldipur

Carina Mallard