Any screening procedure has a characteristic error rate. This is inevitable because in high-throughput screening it is necessary to compromise with some accuracy or precision to achieve the requisite speed. Thus when a large number of compounds are carried through a particular screen, some of the compounds are classified incorrectly.
ERRORS IN SCREENING PROCEDURES
Any screening procedure has a characteristic
error rate. This is inevitable because in high-throughput screening it is
necessary to compromise with some accuracy or precision to achieve the
requisite speed. Thus when a large number of compounds are carried through a
particular screen, some of the compounds are classified incorrectly. A screen
may be used in an absolute sense, so that compounds that pass a certain
criterion are termed positives, whereas those that fail to meet the criterion
are termed negatives. Compounds that pass, but should have failed, are false
positives. In general, false positives are tolerable, if they are not too
numerous, because they will be rectified later. Compounds that fail, but should
have passed, are false negatives. False negatives are lost forever if the
failure eliminates them from further testing.
All screening procedures are based on
assumptions of analogy. They have different degrees of relevance or
predictability. Studies in phase II clinical trials predict the results with
high probability in large clinical trails. But even here there is the
possibility of false-positive or false-negative results. The relevance of a
test is much less in early pharmacological tests, such as used in
high-throughput screening. Generally, the relevance is inversely proportional
to the simplicity of the test.
In any case, one is confronted with the
problem of false-positive results (type I errors) and false-negative results
(type II errors).
In each step, two sources of error for
false-positive results have to be taken in to account:
1.
a = error of the first
type due to the model
2.
α
= error of the first
type due to statistics
In the error of the first type, a compound is
considered to be active, but is actually ineffective. This type of error is
clarified during further development, after negative clinical trials at the
latest.
However, there are two sources of error for
false-negative results:
1.
b = error of the second
type due to the model
2.
β
= error of the second
type due to statistics
In the error of the second type, a compound is
considered to be ineffective, but is actually effective.
This type of error will never be clarified; an
effective drug has just been missed. Perhaps another investigator will test
this compound under different aspects.
The statistical errors derive from the fact
that a phar-macological test is performed only several times or in a limited
number of animals. One can specify the probability that a decision made is
incorrect, that is, a drug candidate is erroneously identified as effective
when it is actually ineffective. Usually this risk is set to 5% (P < 0.05) and is called the
statistical error of the first type or type I error. The error of the second
type or type II error is connected to the type I error by statistical rules.
Usually, screening is performed sequentially.
Tests in high-throughput screening are followed by tests in isolated organs,
then in small animals, and special tests in higher animals, until the compound
is recommended for further development and for studies in human beings. From
each step, not only errors of type I, but also from type II, arise. As a
consequence, many effective compounds are lost.
There are two ways to circumvent this
obstacle: (1) to increase the number of compounds entering the screening
procedure dramatically, hope for a reasonable number of true positives, and
accept a high rate of false-negative results (White 2000) as followed in the
ultra high-throughput screening; or (2) to perform tests with high relevance,
meaning tests with high predictive value in whole animals at an early stage
(Vogel and Vanderbeeke 1990).
The literature on high-throughput screening
includes some publications dealing with false-negative results (Jones and King
2003; Colland and Daviet 2004; Heller-Uszynska and Kilian 2004).
Zhang et al. (1999, 2000) studied the role of
false-negative results in high-throughput screening procedures. They presented
a statistical model system that predicts the reliability of hits from a primary
test as affected by the error in the assay and the choice of the hit threshold.
The hit confirmation rate, as well as false-positive (representing substances
that initially fall above the hit limit but whose true activity is below the
hit limit) and false-negative (rep-resenting substances that initially fall
below the hit limit but whose true activity is in fact greater than the hit
limit) rates have been analysed by computational simulation. The Z-factor and the Zi-factor were introduced to characterize the reliability of high-throughput assays.
The problem of type II errors, that is,
false-negative results, also exists in many other physiological and
phar-macological studies (Martorana et al. 1982; Bar- ros et al. 1991;
Sandkühler et al. 1991; Waldeck 1996; Williams et al. 1997). For example,
Pollard and Howard (1986) reinvesti-gated the staircase test, a well-accepted
primary screening method for anxiolytics, and found several false-negative
results for clinically active anxiolytics.
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