Nice evaluation of the relevancy of surrogate markers by Tim Peto, an
expert in this area. He makes some very interesting comments on
circulating viremia as measured by the viral load tests.

He makes the following four points about measuring plasma viremia:

"Firstly, it is unclear whether circulating plasma viral products (RNA,
viral protein or intact virions) all give internally consistent
results."

"Secondly, it is unclear whether plasma viral load is the main measure
of HIV disease activity."
. . .
"Thirdly, the quantitative relationship between changes in viral load
and drug efficacy is completely unknown."
. . .
"Lastly, the relationship between drug efficacy and changes in viral
load may differ between different classes of antiviral drugs."

Peto concludes his article by stating "At present there is no convincing
evidence that the current surrogate markers can be reliably used to
predict the clinical efficacy of new treatments. Indeed proper
validation will probably need to await the arrival of much more
effective clinical treatments. Meanwhile, surrogate marker responses
should only be used in the early assessment of new drugs, in order to
help the selection of new drug regimens for large scale testing in long
term clinical trials."

===================================

Peto T. Surrogate markers in HIV disease. Journal of Antimicrobial
Chemotherapy 1996, May, 37(Suppl. B):161-170.

Abstract: The use of surrogate markers in HIV disease is an attractive
method of assessing the efficacy of new treatments more quickly than by
using clinical end-points. The characteristics of an ideal surrogate
marker and the theoretical dangers of extrapolating properties from one
class of drug to another are described. These characteristics are
compared with the use of the CD4 lymphocyte count, which so far has been
the most widely studied. Results from 14 randomized controlled trials of
nucleoside analogues are used to compare the comparative changes of CD4
counts with the differential rates of progression to AIDS and
differences in survival. There was some correlation between CD4 count
changes and development of AIDS, particularly in the short term trials.
In contrast, there was little correlation between CD4 counts and overall
survival. Comparative studies between clinical end-points and
quantitative measures of plasma viraemia have not yet been completed. In
conclusion, no surrogate marker has yet been shown to be useful in
predicting the efficacy of anti-HIV treatment. Until surrogate markers
are validated against the results from long term clinical trials, they
should only be used to screen new drugs warranting further study rather
than to draw conclusions on the clinical efficacy of new treatments.

Page 163-4: COMPARISON OF DIFFERENT TREATMENTS. Surrogate markers can be
used to determine the clinical efficacy of a new treatment. This is the
most stringent requirement for a surrogate marker. It has been suggested
that for a surrogate marker to be valid ". . . a test of the null
hypothesis of no relationship [of the marker] to the treatment groups
must also be a valid test of the corresponding null hypothesis based on
the true endpoint." For a surrogate marker to satisfy this, changes over
time must correlate with the risk of progression and any effect of
treatment on the risk of clinical progression must be explainable and
predictable by the effect of treatment on the markers. For example, CD4
lymphocyte counts are a valid marker only if the whole treatment effect
of zidovudine is reflected by changes in the CD4 counts. Unfortunately
observational studies have failed to show this.

Even this stringent requirement of a surrogate marker has its
limitations. It is also important to have data on the size of a
treatment benefit in order to determine whether the benefit outweighs
its toxicity and cost. Such a quantitative relationship between changes
in the surrogate marker and the clinical outcome is difficult to
predict. A common fallacy is to assume that the relationship between a
prognostic marker and outcome, obtained from natural history studies,
can be used to predict the effect of a treatment induced change in the
marker.

The different possible relationships which might be found between a
change in a surrogate marker and the clinical outcome is represented
schematically in Figure 1. Each relationship represents the response of
the same marker to different classes of drugs. The horizontal axis
represents the effect of treatment on the value of the drugs. The
horizontal axis represents the effect of treatment on the value of the
surrogate marker while the vertical axis represents any improvement (or
worsening) in clinical outcome. In the diagram some responses are
compatible with a ‘Prentice’ marker. For example drug ‘O’ in ineffective
with no effect on the marker nor on outcome. Drug ‘I’ produces a
dose-response curve of an ideal surrogate marker in which there is a
continuous and predictable quantitative relationship between the change
in the marker and clinical effect. Drug ‘H’ produces a large change in
the marker with little benefit, while drug ‘G’ is clinically effective
but only has a marginal impact on the surrogate marker. Drug ‘B’ worsens
both outcome and the marker. The other drugs (A,C,E,D) do not follow the
expected surrogate-clinical relationship. Drugs E and C affect clinical
outcome without changing the marker. Pathophysiological explanations are
not difficult to conceive: for instance, a clinical effective drug (E)
could affect the distribution of lymphocytes (or virus) between the
circulation and other tissues. An inactive drug (C) could be dangerously
toxic. Other clinically ineffective drugs could simply affect the marker
as an ‘epiphenomenon’.

Drugs may have the opposite effect than expected (for example, A and D).
Drug D looks attractive as judged by the surrogate response but is
clinically deleterious. This pattern of response has often, and
dramatically, been shown in other diseases. In particular, in heart
disease, flecainide was shown to prevent arrhythmias which are
prognostically a dangerous sign but perversely was shown in a large
randomised study to increase mortality (Cardiac Arrhythmia Suppression
Trial, 1989). Drug A is an important class of drug which likely to have
a novel action producing clinical benefit in a manner that worsens the
prognostic markers. It is not implausible that a new ‘miracle drug’
which kills all HIV infected cells would cure the patient but might
produce a transient although profound fall in circulating CD4 cells and
an intense rise in the measured plasma viral load. Such a paradoxical
effect on prognostic markers is illustrated by the effect on haemoglobin
and white blood counts of chemotherapy for the treatment of leukaemias.
The response of a surrogate marker to a new class of anti-HIV therapy
should therefore be interpreted with caution until its relationship with
the outcome has been determined in clinical trials.

Pages 165-166: CIRCULATING VIRAEMIA. The advantages of measures of viral
load are that they offer a pathophysiologically plausible index of
disease activity and that they are easy to quantify. They are now
commonly used to screen the clinical activity of new drug combinations.
Repeated measurements can be easily performed on each patient. Repeated
measurements can be easily performed on each patient and changes in
viral load accurately followed over weeks and months. Therefore, only a
small number of patients need to be studied to obtain a repeatable
results.

The disadvantages, however, of the assays are fundamental. Firstly, it
is unclear whether circulating plasma viral products (RNA, viral protein
or intact virions) all give internally consistent results. Secondly, it
is unclear whether plasma viral load is the main measure of HIV disease
activity. Recent work has suggested that the main reservoir of HIV
disease is in the lymph nodes. Although observational studies suggest
that plasma viral load gradually increases with disease progression, the
importance of circulating viral load is unclear. It is possible that
most viruses reside in tissue cells and only use circulatory cells to
traffic between tissues. It is also possible that cell-free viral load
represents the products of cell death and therefore might be expected to
rise in the face of effective anti-HIV therapy. Thirdly, the
quantitative relationship between changes in viral load and drug
efficacy is completely unknown. There is a reasonable belief that the
more viral load is suppressed and the more prolonged the trough, the
better. However, this assumption has never been tested. Recent studies
on the kinetics of viral replication suggest that viral turnover is very
marked. This makes any inference from viral load measurements to total
body viral replication difficult. For instance, drugs which increase the
clearance of virions from the plasma will lead to an observed fall in
virion concentration, which in turn may lead to a false conclusion that
total body viral replication has been suppressed.

Lastly, the relationship between drug efficacy and changes in viral load
may differ between different classes of antiviral drugs. Although a
rational relationship may exist between viral load changes and efficacy
for nucleoside analogue reverse transcriptase inhibitors, the same
relationship may not hold for other reverse transcriptase inhibitors,
let alone protease inhibitors. Clearly, the expected short term effect
on circulating viral load of effective gene therapy strategies or other
novel treatments (for example, the transfer of HIV 1 specific cytotoxic
lymphocytes), is completely unclear. The interpretation, therefore, of
changes in the markers of disease activity with novel treatments is
difficult and may lead to major errors. Current large scale randomised
clinical trials of combination treatment (DELTA and ACTG 175) are
designed to compare changes in viral load, as judged by these new
virological assays, with clinical outcome.

Page 169. CONCLUSION. At present there is no convincing evidence that
the current surrogate markers can be reliably used to predict the
clinical efficacy of new treatments. Indeed proper validation will
probably need to await the arrival of much more effective clinical
treatments. Meanwhile, surrogate marker responses should only be used in
the early assessment of new drugs, in order to help the selection of new
drug regimens for large scale testing in long term clinical trials.
_____________________________________________________________________

         TABLE I. EXAMPLES OF MARKERS OF DISEASE PROGRESSION
_____________________________________________________________________

 Measure of CD4 body mass
  Landmark levels of CD4 count, %CD4 count, CD8, CD4/CD8 ratio

 Immune activation
  beta 2 microglobulin
  neopterin

 Immune dysfunction
  cutaneous anergy

 Clinical event as surrogate markers for disease progression
  symptoms of ARC
  first AIDS defining symptoms
  incidence of recurrent AIDS events
  TB
  weight
  Karnofsky score

 Virological markers of immune dysfunction
 syncytium-inducing HIV-phenotypes
 Plasma HIV RNA load
_____________________________________________________________________

              TABLE II. MARKERS OF DISEASE ACTIVITY
_____________________________________________________________________

 p24 antigen
 p24 antibody
 Plasma viraemia
 Plasma RNA
 Evolution of viral resistance

 Circulating cellular viral markers

 Lymph node viral load

 Short term changes in CD4 counts, CD8 counts, CD4:CD8 ratio, cellular
  immunity
_____________________________________________________________________