1. health and fitness
2. disease

PERSPEC TIVES
10. British Broadcasting Corporation. Moorfields
hospital carries out world’s first gene therapy
operation to cure blindness. <http://www.bbc.
co.uk/pressoffice/pressreleases/stories/2007/05_
may/01/moorfields.shtml>
(1 May 2007). Accessed 1 July 2007.
11. Piquette-Miller, M. & Grant, D.M. The art and
science of personalized medicine. Clin. Pharmacol.
Ther. 81, 311–315 (2007).
12. Guttmacher, A.E. & Collins, F.S. Realizing the
promise of genomics in biomedical research.
JAMA 294, 1399–1402 (2005).
13. National Cancer Institute and National Human
Genome Research Institute. The Cancer Genome
Atlas <http://cancergenome.nih.gov/index.asp>.
Accessed 1 July 2007.
14. Food and Drug Administration. Guidance for
Industry: Pharmacogenomic Data Submissions
<http://www.FDA.gov/cber/gdlns/pharmdtasub.
htm> (March 2005). Accessed 1 July 2007.
Beyond Genomics
CT Dollery1
The sequencing of the human genome has already had an enormous
impact on medicine, particularly with single-gene changes
that predispose to a serious disease such as cystic fibrosis or the
overexpression of Her2 in about one-third of breast cancers. Genetic
technology has led to some very important therapeutic innovations,
including the use of imatinib mesylate (Gleevec) in BCR-ABL chronic
myeloid leukemia and of trastuzumab (Herceptin) in Her2-positive
breast cancer, but the much anticipated explosion of new effective
treatments has been more modest than expected.
Scientific meetings are now held regularly
to bemoan the decline in registrations of
new drugs with regulatory agencies such
as the Food and Drug Administration and
to explore the reasons. Many explanations
have been put forward, including excessively stringent regulation, limitations of
highly automated industrial drug discovery methods, and unimaginative clinical
trials. All these potential explanations
have their advocates, but the realization is
slowly dawning that the biggest problem is
lack of good targets because of the limited
understanding of the etiologic mechanisms of most common diseases.
Silver bullet or carronade?
Cancer is the ultimate genetic disease, and
sequencing the human genome would have
been justified purely for the insights it gave
into oncology. But apart from virology and
cancer, genetics has not thus far been nearly
as helpful as was expected in other disease
areas. A few have drawn the uncomfortable
conclusion that the underlying explanation
doi:10.1038/sj.clpt.2007.6100363
366
Beyond genomics?
for the lack of success in defining new targets may be that most common diseases are
driven by the coincidence of multiple factors without one being dominant. A quote
from a recent article in Nature describing
a large genetic study of seven common diseases serves to illustrate this point: “For any
given trait there will be few (if any) large
effects, a handful of modest effects, and
a substantial number of genes generating
small or very small increases in disease
risk.”1 If that is the case, the existing paradigm of drug discovery and development
requires radical rethinking, as does the
concept of personalized medicine based
solely on genetics. Exit the silver bullet
and enter the carronade (the carronade
was a short-barrel, large-diameter cannon
that could be loaded with a canister of 500
musket balls).
Nowhere is the lack of progress in
understanding mechanisms underlying
the etiology and maintenance of most
common disease processes felt more
keenly than in the pharmaceutical indus-
1GlaxoSmithKline, Harlow, UK. Correspondence: CT Dollery ([email protected])
try. The cascade of new “druggable targets”
disclosed by the sequencing of the human
genome (>300 G protein–coupled receptors, >500 kinases, and a similar number
of proteases)2 has had a much more limited impact than early enthusiasts anticipated. The attrition rate of pharmaceutical
projects aimed at these novel targets is very
high (estimated to be ∼95% failure rate for
drugs aimed at novel targets), and one of
the most common reasons given is “lack of
efficacy.” In many cases this is a misnomer:
the drug had the anticipated pharmacological action, but the effect on the disease
mechanism was small or nonexistent. A
better way of describing the result would
be “poor choice of target.”
Many human diseases reflect a disorder
in physiologic processes such as blood
pressure, body weight, or inflammation
that are known to involve the interaction of many complex control loops and
to respond to some degree to a variety
of pharmacologic agents and environmental factors. It is highly unlikely that
the genome of the average greater than
third-generation American has undergone much change in the last 75 years,
and the same proportion must carry the
FTO gene,3 but the incidence of obesity and type II diabetes has increased
markedly. Agents as diverse as diuretics,
α- and β-adrenergic antagonists, angiotensin-converting enzyme inhibitors,
angiotensin II antagonists, and calcium
L channel blockers lower blood pressure
in hypertension to some degree, but none
of them cures it, and disorders of these
control systems have not been identified
as playing a major function in its etiology.
Against this background it should have
been anticipated that the genetic influences in these conditions were likely to
be complex and multifactorial. The optimists argue that all that will be needed is
to subdivide the existing broad phenotype
into several new diseases in each of which
only one or two gene polymorphisms will
be major etiologic factors. The pessimists
(realists?) argue that everyone will have
a mix of environmental and genetic risk
factors, and although the mix will vary,
multiple processes will be at work in
almost all.
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PERSPEC TIVES
Renaissance of integrative physiology
and pharmacology
Ultimately the choice of drug targets must
rest on their ability to modify a disease
process, but a critical step toward that
objective is to understand the normal
physiologic functions of target molecules.
A large part of the current problem in
drug discovery is that the productivity of
genomics technology, for a time, far outran biological research capacity to translate this information into insights about
the role of a gene product in normal physiology. This imbalance is beginning to be
addressed by using genomic techniques
such as quantitative gene expression
(mRNA), gene knockout, tissue-specific
gene inactivation (CreLox), small interfering RNA (siRNA) gene silencing, and
monoclonal antibodies combined with
accurate physiologic measurements to
understand the tissue distribution and
physiologic function of gene products.
In humans it has been necessary to rely
more on experiments of nature in the
shape of genetic polymorphisms.
One consequence of the renaissance in
experimental physiology has been to reveal
a severe shortage of physiologists trained in
the study of integrated systems in intact animals and humans. The importance of these
developments to pharmacology should not
be underestimated. Pharmacology grew out
of physiology, and a renaissance of integrative physiology should be matched by a
similar commitment to integrative pharmacology both preclinically and clinically.
A word of caution is in order, though, and
it pertains to an additional reason for quality clinical pharmacology. The function in
humans of a protein that has a close (or
identical) animal homologue may differ
substantially, as was shown with the CD28
antibody TGN1412 that was relatively
benign in cynomolgus monkeys but caused
a devastating cytokine storm in humans.4
Disease models in preclinical species
as predictors for human pathophysiology
are another story. They worked well for
deficiency diseases, bacterial infections,
hypertension, and some types of inflammation. In an era in which the major disease targets are conditions such as virus
infections, cancer, dementia, schizophrenia, and osteoarthritis, their value is much
more limited. Whether these models can be
made more useful by knocking in human
genes to preclinical species remains to be
seen, in that reproducing the complexity
of human disease etiology in a mouse will
be of daunting complexity. For the present
the chief experimental animal for understanding human pathophysiology is the
human, and clinical pharmacology has an
important role to play by using drugs to
illuminate mechanisms.
Aghast at the complexity?
Sidney Brenner, a Nobel laureate, once
said that the problem of biology is not
to stand aghast at the complexity but to
conquer it.5 It is also a fitting challenge
for clinical pharmacologists faced with
the challenge of making drug therapy
safer and more effective in the real world.
Where should they start? One obvious
answer is that they cannot do it alone. A
multidisciplinary team approach will be
essential, ranging from biostatisticians
to practitioners of family and specialist
medicine, but the two key components are
knowledge of the drug action in humans
and detailed knowledge of the patient taking the drug. The former is, or should be,
the purview of the clinical pharmacologist.
The latter requires a strong background
in clinical medicine either through personal knowledge and training as part of
a clinical pharmacology program or with
a committed clinical collaborator. Either
way, a deep understanding of the often
complex clinical situation is necessary.
If clinical pharmacologists are to fulfill
a front-line role in the study of drugs in
patients and make a major contribution to
personalized medicine, the retreat into the
laboratory and to studies largely confined
to healthy volunteers—not patients—will
have to be reversed.
Back to the clinic
Some clinical pharmacologists will participate directly in clinical care, because
they also have trained in a specialty such
as oncology, psychiatry, or cardiology. For
them, direct access to patients is an obvious
advantage. Others will participate in clinical research in a more collaborative role,
but even if they are not themselves medically trained, the acquisition of a thorough
knowledge of the disease and patients
under study will greatly strengthen their
CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 82 NUMBER 4 | OCTOBER 2007
contribution to clinical research and the
willingness of clinical colleagues to collaborate. Where should they start?
Investigation of outliers
Pharmaceutical companies sponsoring
research on their compounds, regulatory authorities considering whether to
license them, and purchasing agencies
considering whether to authorize their
use in local formularies pay most attention to the magnitude of benefit and its
statistical significance for the whole group
of patients in a clinical trial. For the clinical investigator looking for clues, a better place to start is to investigate outliers.
These could be patients suffering a serious
adverse reaction to a medicine that is safe
in the great majority. Several important
but rare polymorphisms of drug metabolism have been discovered in this way,
including the dihydropyrimidine dehydrogenase polymorphism and 5-fluorouracil (5-FU) toxicity.6 But this is not the
only mechanism of severe 5-FU toxicity.
The interaction between the anti–herpes
zoster drug sorivudine (5-bromovinyl-ara
uracil) and standard doses of 5-FU, which
resulted in 18 deaths, was another salutary
example that might have been foreseen.7
The outlier approach need not be confined
to adverse reactions; it can also be applied
to clinical trial data to investigate, say, the
top 5% to 10% of excellent responders and
the bottom 5% to 10% who had little or
no response. Lessons learned from outliers often prove to have applications to
the mass of patients who are not outliers.
Design of clinical trials
The whole fabric of evidence-based medicine and the Cochrane database rests on
large, well-designed, randomized controlled clinical trials. Historically, clinical pharmacologists have only a limited
impact on the design of such trials. This
may account for the relative neglect of
important issues such as dose-response or
concentration-response, for both efficacy
and side effects, in many large trials. Fortunately, the situation is changing with the
growing use of pharmacokinetic-pharmacodynamic (PK/PD) analyses in assessing
individual differences in response. This is
not merely a matter of combining pharmacodynamic and pharmacokinetic data,
367
PERSPEC TIVES
because the relationship is often far from a
simple log-dose response curve and may be
quite different for efficacy parameters and
adverse effects more directly related to the
pharmacology. A knowledge of the clinical
pharmacology and its relationship to the
disease mechanism is essential both in the
design and the analysis of clinical trials to
secure the best yield of useful information
and the most valuable interpretation of
the data. Clinical pharmacologists are, or
should be, much more interested in accurate recording of symptomatic side effects
as part of the spectrum of pharmacologic
activity than the epidemiologists, statisticians, and clinician specialists who usually
have their main attention devoted to demonstrating efficacy.
One important additional skill the clinical pharmacologist can bring is to increase
the accuracy of individual measurements
in clinical trials. Most measurements made
in large clinical trials are relatively imprecise. The objective is to show a significant
effect for the group as a whole. To gain useful information for personalized genetic
studies, individual PK/PD analysis, and
the like, the accuracy of individual measurements will have to be improved. It is
already an issue in adaptive trial designs. It
will become even more critical as we move
more toward factorial designs of multidrug
combinations in relatively small groups
of patients.
Personalized medicine
When a subway train stops in London,
overseas passengers are often amused
when the station loudspeakers start playing a message to “mind the gap” between
the train and the curved platform. Two
recent edited conversations illustrate the
gap in understanding of the background
to personalized medicine.
The first was with a senior, very experienced internist who had just finished
reading a journal article about personalized medicine. She mused aloud that she
thought she had practiced personalized
medicine throughout her professional life.
She took a careful medical, family, and
social history including a detailed account
of any current treatment or recent past
therapy, alcohol and tobacco intake, and so
on. Most of her patients were over 60 years
old, and it was a rarity that they were being
368
treated for only one medical condition, so
most were on several drugs. She dreaded
the moment when an elderly patient produced a bag containing a large assortment
of prescription and nonprescription drugs
without a clear recollection of which he or
she was still taking and when. Her greatest
contribution to personalized medicine was
often to have a patient stop taking at least
half of this large assortment of medicines.
The second conversation was with a
younger nonclinical scientist at a pharmacokinetics meeting. He tackled me,
apparently as an elderly symbol of medical
conservatism, to know why all the discoveries about genetics polymorphisms of the
drug-metabolizing enzymes had had so
little influence on medicine. Why didn’t
physicians routinely assess the CYP2D6
polymorphism before prescribing metoprolol, paroxetine, or codeine and measure at least CYP2C9 polymorphism, let
alone VKORC1, before choosing a starting dose of warfarin? Perhaps, he implied,
the coming of the Amplichip CYP450 (ref.
8) gene array chips for drug metabolism
would drag the medical profession out of
the therapeutic dark ages.
Although the gap between the London
subway train and the platform is rarely
more than a few inches, the gap these two
conversations illustrate is much wider,
and we must find ways to bridge it. Both
parties have some right on their side, but
the reductionist approach has important limitations in very complex realworld situations.
Testing hypotheses in personalized
medicine: go for big effects
It is relatively easy to propose strategies
for improving personalized medicine but
much harder to test and implement them.
The physician whose treatment advice to
a new patient starts with “I want you to
stop smoking, halve your alcohol intake,
and lose 30 pounds” soon learns from
direct personal experience that this will
not work, in a small number of patients.
In most cases, it will be more difficult than
this simple example to test new strategies
in personalized medicine and will require
large-scale testing in clinical trials before
anyone will take the new ideas seriously.
Those familiar with calculating the numbers required in clinical trials will know
how large these can be if the intention is to
demonstrate superiority for a new medicine that is expected to have, say, a 10%
advantage over an existing medicine for a
common disease.
Similar considerations will apply to testing personalized medicine strategies. There
must be good grounds, based on careful
modeling, for anticipating a sizable effect.
For example, a major metabolic route for
metoprolol is via CYP2D6, and in patients
with polymorphisms that reduce its function, plasma concentrations are higher,
sometimes much higher. However, within
groups with similar genotypes concentrations also vary widely. To convince a
health-care purchaser to use CYP2D6
genotyping routinely when prescribing
metoprolol would probably require the
design of a trial to show not just that the
concentration achieved was more consistent but that this was sufficient to produce
a worthwhile clinical improvement in the
control of angina or hypertension. Given
the high variability within genotypes this
would be difficult, although not necessarily impossible. But it makes the point that
the first thing to do when considering a
new strategy for personalized medicine is
to estimate the contribution of the factors
under study to the variance in response
and concentrate on those that have the
potential to make a big difference. Step one
is to estimate the effect on the variance in
the pharmacological mechanism but step
two is to model the effect that change in
pharmacology would have on the therapeutic response. Both are areas in which
the clinical pharmacologist ought to be able
to make a major contribution.
Integrating knowledge, building models
At the battle of Trafalgar a canister of musket balls from the 68-pound carronade on
Horatio Nelson’s flagship killed or wounded
hundreds of men operating the guns on the
French flagship. Moments later, a French
sharpshooter fatally wounded Nelson
with a single musket ball. Once battle was
underway, which was the more significant
shot? Nelson died of his wound, but the
French flagship was captured.
For more than 50 years medical educators have taught their students to abhor
blunderbuss therapy and choose the
elegant, carefully selected, single-drug
VOLUME 82 NUMBER 4 | OCTOBER 2007 | www.nature.com/cpt
PERSPEC TIVES
intervention. But we now live in an era
when well-known epidemiologists advocate a “polypill” for preventing heart disease9 and patients with many common
diseases (hypertension, diabetes, cancer)
are routinely treated with more than one
drug. The wide-ranging implications of
these changes have received little attention
from clinical pharmacologists apart from
studies of interactions in drug metabolism.
Yet logical, well-conceived drug combinations may prove to be the best solution to
diseases driven by multiple etiologic factors in complex control systems. Patents
are already appearing for devices that are
intended to dispense individualized “polypills.” Some of the drivers for a new era of
scientifically credible polypharmacy will be
genetic and some environmental; often the
end result will be determined by the play of
one on the other. This may turn out to be
the real personalized medicine.
The classical reductionist approach of
science to a complex problem is to stabilize as many variables as possible and alter
one, judged to be important, to see what
happens to another that has not been stabilized. In the early days of experimental
physiology and pharmacology of the cardiovascular system, this often worked brilliantly. But the major unmet clinical needs
of today (osteoarthritis, diabetes, lung cancer, chronic obstructive pulmonary disease,
schizophrenia, dementia, etc.) present a
more difficult challenge and have not thus
far proved particularly amenable to traditional experimental approaches. Nor has
genetics identified single factors making a
major contribution to the variance.
An alternative approach is to focus
efforts on the pathways and the control systems that influence the disease state under
study to identify nodes particularly suitable
for intervention. It is a complex task but
potentially very rewarding. Might some of
those compounds directed at that G protein–coupled receptor or this kinase, and
discarded as ineffective, prove to be useful
as part of a carefully chosen combination
strategy? Might a mutation in one part of
a control system that is not readily accessible to intervention be circumvented by
modulating another control loop with
which it interacts?
These data can be used as a basis for
building a mathematical model of the dis-
ease that will require successive iterations
between model and experiment to test it.
This approach, now termed systems biology, is bound to require a great deal of
experimental work in humans, perhaps
using tool compounds not fully optimized
as drugs and very sophisticated measuring
techniques to assess effects over relatively
short periods of drug administration.
Populations are our paymasters and our
laboratory
In an increasingly cost-conscious healthcare world, the benefit and risk of therapeutic interventions are being closely
studied. Bodies such as the National Institute of Health and Clinical Excellence in
the United Kingdom10 have set a notional
maximum the British National Health
Service will pay for one additional quality-adjusted year of life (currently said
to be ∼$60,000). Some purchasers argue
that they should pay only for medicines
that have been demonstrated to benefit
the individual patient. These are powerful
forces and will mobilize pressure to devise
better methods of identifying responders
and nonresponders among the generality
of patients, not just the “sanitized” individuals recruited into clinical trials. Clinical pharmacologists ought to be in the
forefront of work on these areas alongside
health economists, but in doing so they
will have to face some new realities.
In the United States it is estimated that
more than half of patients do not comply
with prescription instructions and almost
20% of prescriptions are never filled.11 The
frequency of refilling prescriptions suggests
that many patients who do take their medicine fairly regularly are taking appreciably
less than the prescribed dose. Many of the
patients will be elderly and under treatment for more than one disease, each with
more than one drug. The noise level in the
data will be very high, but the problem is
not insurmountable. The dramatic impact
that new drugs have had, particularly on
cardiovascular disease and HIV, shows that
big signals can penetrate the noise.
Very large patient databases will become
important tools. At present these are usually mined for safety information on a single drug, but there is considerable potential
to study the effect of combinations, both
planned and incidental, on efficacy. Statis-
CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 82 NUMBER 4 | OCTOBER 2007
tical and epidemiologic expertise will be
essential, but so will knowledge of clinical pharmacology and internal medicine.
Novel hypotheses generated in this way
will require modeling using systems biology approaches, and sophisticated testing
in the clinic.
A new dawn
Gloom about the present situation of drug
discovery has been much exaggerated. Its
origin was an unrealistic assessment of
the time scale and biological complexity
involved in applying the new knowledge
from the sequencing of the human genome.
In retrospect, sequencing was the easy part.
But we know more about biology today
than ever before. Our ability to make rapid
and accurate diagnoses of human disease
has made enormous progress powered,
particularly, by imaging technology. The
fusion of genetic and molecular techniques
with integrative physiology and pharmacology is beginning to pay off with exciting
new discoveries. These will translate into
much better understanding of normal control systems and the way pharmacologic
agents interact with them, for example, the
varied ways that different anesthetic agents
acting on the γ-aminobutyric acid receptor
can interfere with memory12 or the marathon mouse construct with peroxisome
proliferative activated receptor δ.
Our understanding of the mechanisms
responsible for the etiology and progression of human disease (not necessarily the
same) has lagged somewhat, but many of
the tools needed to advance experimental
medicine and clinical pharmacology now
exist. We are not “beyond genomics” but
over the hump of unrealistic optimism.
There will be major payoffs, it may take
longer than we hoped, and it will require
an enormous amount of hard work and
large sums of money, but we must not
let the patients down who so badly need
better medicines.
CONFLICT OF INTEREST
The author declared no conflict of interest.
© 2007 ASCPT
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2.
The Wellcome Trust Case Control Consortium.
Genome-wide association study of 14,000 cases
of seven common diseases and 3,000 shared
controls. Nature 447, 661–678 (2007).
Hopkins, A.L. & Groom, C.R. The druggable
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Frayling, T.M. et al. A common variant in the FTO
gene is associated with body mass index and
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Suntharalingam, G. et al. Cytokine storm in
a phase 1 trial of the anti-CD28 monoclonal
antibody TGN1412. N. Engl. J. Med. 355, 1018–
1028 (2006).
Academy of Medical Sciences and The Royal
Academy of Engineering. Systems Biology: A
Vision for Engineering and Medicine <http://
www.raeng.org.uk/policy/engagement/pdf/
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Wei, X., McLeod, H.L., McMurrough, J., Gonzalez,
F.J. & Fernandez-Salguero, P. Molecular basis of
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Okuda, H. et al. Lethal drug interactions of
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11. Krueger, K.P., Felkey, B.G. & Berger, B.A. Improving
adherence and persistence: a review and
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The mitochondrial PTP as a target for
modulating cell death
Targeting Cell Death
DJ Hausenloy1 and L Scorrano2
Functional consequences of myocardial or cerebral infarction are
the result of excessive cell death. It is patent that preventing cell
death is the therapeutic goal in any ischemia-reperfusion setting.
Mitochondria amplify apoptotic cascades and have emerged as crucial
organelles in ischemia-reperfusion. Changes in mitochondrial inner
membrane permeability and in the morphology of the organelle are
regulated, perhaps interconnected processes that are starting to
emerge as novel therapeutic targets for reducing cell death induced by
ischemia-reperfusion.
Apoptosis is a genetic program of cell death,
conserved among all metazoans, required
for normal embryonic development, and
essential to maintain tissue homeostasis by
offsetting cell division. Malignant cells are
able to evade death and proliferate by counteracting endogenous death mechanisms.
Therefore, promoting death of tumor cells
is the underlying aim of most anticancer
therapies. Conversely, cell death induced
by pathological stressors such as ischemiareperfusion as well as environmental toxins and genetic lesions predisposing to
neurodegeneration is clearly detrimental.
Ischemia-reperfusion in the context of a
myocardial or cerebral infarction has seri-
ous clinical repercussions; the therapeutic
aim is therefore to prevent or reduce cell
death. This limits the extent of the infarct
and improves clinical outcomes, related to
the size of the infarcted area.
In an acute myocardial infarction (AMI),
the prolonged acute myocardial ischemia
results in cardiomyocyte death. Because the
duration of acute myocardial ischemia is a
major determinant of infarct size, removal
of the thrombotic occlusion and restoring
coronary artery flow is the most effective strategy for salvaging viable myocardium. Myocardial reperfusion is currently
achieved by thrombolytic therapy or primary percutaneous coronary intervention,
1The Hatter Cardiovascular Institute, University College London Hospital, London, UK; 2Dulbecco-Telethon
Institute, Venetian Institute of Molecular Medicine, Padova, Italy. Correspondence: L Scorrano (lscorrano@dti.
telethon.it)
doi:10.1038/sj.clpt.2007.6100352
370
strategies that have markedly improved the
clinical outcome following an AMI. However, the restoration of coronary blood flow
paradoxically induces further cardiomyocyte death, thereby reducing the overall
benefits of myocardial reperfusion—a
phenomenon termed lethal reperfusion
injury. Mounting pharmacological and
genetic evidence indicates that a critical
mediator of this form of cell death is the
mitochondrial permeability transition pore
(PTP), whose opening early in the course
of myocardial reperfusion disrupts mitochondrial function and activates the postmitochondrial apoptotic cascade.
The PTP is a nonselective, tightly regulated
high-conductance channel of the inner
mitochondrial membrane.1 Its opening collapses the mitochondrial membrane potential, depletes the mitochondrial NADH
pool, and uncouples oxidative phosphorylation, leading to a vicious circle of ATP
hydrolysis and depletion, release of caspase
cofactors and activators, and ultimately to
cell death.2 The actual composition of the
PTP is a matter of debate. Previous models
that included the voltage-dependent anion
channel (VDAC) of the outer membrane
and the adenine nucleotide translocase
of the inner membrane have been challenged by genetic studies, suggesting that
neither VDAC3 nor adenine nucleotide
translocase4 is an obligatory component
of the PTP. In contrast, the matrix protein
cyclophilin-D (CypD) has been found
to be a strong facilitator of PTP opening,
and its ablation results in increased resistance to the PTP inducer Ca2+, yet not to
the absence of PTP.5 Cells lacking CypD
were found to be resistant to ischemiareperfusion damage, both in the brain and
in the myocardium; 6,7 on the other hand,
the response to classic intrinsic apoptotic
inducers was unaffected, a result that
was interpreted to exclude the participation of the PTP in the core mitochondrial
death pathway of apoptosis.8 A detailed
discussion on the precise role of PTP in
the mitochondrial phase of apoptosis is
beyond the scope of this article. Nevertheless, this genetic evidence strongly suggests
that PTP is crucially involved in cell death
during ischemia-reperfusion damage,
VOLUME 82 NUMBER 4 | OCTOBER 2007 | www.nature.com/cpt