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Hi Matt,<br><br>
I agree with what was said before, but would like to add an aspect.
<br><br>
Physically, the information about oligomers resides in the slope of the
solution plateaus. This can be addressed with very high precision,
because there's a lot of data points in those plateaus. One can
easily simulate noisy data and ask what fraction of oligomer could be
detected in the best-case scenario, assuming a perfect experiment.
Other people have done that, too; I did this for a simulated antibody
trimer, assuming a signal/noise ratio of 200:1 (i.e. 0.005 fringes noise
and 1 fringe loading concentration), and found that 0.2% should be
detectable. For something that sediments closer to the leading edge
of the monomer boundary, it might get more difficult, but that would
depend also on how well you can characterize the monomer (maybe that can
be done separately). What I would recommend is to use the SEDFIT
integration tool and do a Monte-Carlo statistics on the integrated
values. Alternatively, I would probably also try the SEDPHAT hybrid
discrete/continuous model, describe the monomer as a discrete species
(perhaps predetermined or constrained in a global fit including data from
pure monomer), and describing the larger material as a continuous section
of c(s) [local to each data set]. <br><br>
It is a valid question whether or not in a real experiment you can reach
the theoretical limit, but my feeling from talking to several colleagues
is that with careful experimentation you should get close, provided also
that the sample is behaving hydrodynamically ideal. (Clearly, you
should not rely on the details of the c(s) analysis if it doesn't give a
good fit.)<br><br>
If you're asking separately how much dimer, trimer, or tetramer etc.
exists, this is more difficult to answer than the question "how much
material sediments in a certain s-range", or "how much total
oligomers are there". The latter question can be addressed
with much higher precision, since by integrating over a large s-range
(which would exclude the monomer) you essentially are not relying on
fitting individual boundaries into the slightly sloping plateaus, but
instead essentially only measuring the total increase of the plateaus
(which corresponds to the loading concentration of large
material). This is what the integral of c(s) over the s-range
of large material gives you. This is essentially equivalent to
integrating the ls-g*(s), except that with the c(s) you may be able to
perhaps set the range of interest closer to the monomer, and also that
you will be able to use a larger data basis by including scans ranging
from the initial partial depletion at the meniscus until very late where
the trailing edge of the monomer boundary has disappeared (which will
help characterizing the monomer).<br><br>
Regards,<br>
Peter<br><br>
<br><br>
<br><br>
At 04:41 PM 3/23/2006, you wrote:<br>
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<font face="arial" size=2>Hi folks.<br>
<br>
I was
wondering if people could give their views on the degree of precision
that can be expected from the c(s) method, or other methods for fitting
SV data, for assessing the degree of aggregate formation in a protein
sample. (Monoclonal antibodies, in particular.) The FDA is starting to
ask for “orthogonal methods” to back up SEC data on aggregate formation.
Is it reasonable to expect that SV can give the same degree of
reproducibility when dealing with, say, 1 to 5% aggregated species? Has
anybody done any systematic determinations of the precision of the
method? (Subjective impressions are also welcome!)<br>
<br>
Thanks,<br>
<br>
Matt<br>
<br>
Matthew Parker, Ph.D.<br>
Analytical and Pharmaceutical Sciences<br>
Immunogen, Inc.<br>
128 Sydney St.<br>
Cambridge, MA 02139<br>
(617) 444-2028<br>
(617) 995-2544 fax<br>
<a href="mailto:matthew.parker@immunogen.com">
matthew.parker@immunogen.com</a><br>
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