poolHiTS: A Shifted Transversal Design based pooling strategy for highthroughput drug screening
 Raghunandan M Kainkaryam^{1} and
 Peter J Woolf^{1, 2}Email author
https://doi.org/10.1186/147121059256
© Kainkaryam and Woolf; licensee BioMed Central Ltd. 2008
Received: 10 December 2007
Accepted: 30 May 2008
Published: 30 May 2008
Abstract
Background
A key goal of drug discovery is to increase the throughput of small molecule screens without sacrificing screening accuracy. Highthroughput screening (HTS) in drug discovery involves testing a large number of compounds in a biological assay to identify active compounds. Normally, molecules from a large compound library are tested individually to identify the activity of each molecule. Usually a small number of compounds are found to be active, however the presence of false positive and negative testing errors suggests that this onedrug oneassay screening strategy can be significantly improved. Pooling designs are testing schemes that test mixtures of compounds in each assay, thereby generating a screen of the whole compound library in fewer tests. By repeatedly testing compounds in different combinations, pooling designs also allow for errorcorrection. These pooled designs, for specific experiment parameters, can be simply and efficiently created using the Shifted Transversal Design (STD) pooling algorithm. However, drug screening contains a number of key constraints that require specific modifications if this pooling approach is to be useful for practical screen designs.
Results
In this paper, we introduce a pooling strategy called poolHiTS (Pooled HighThroughput Screening) which is based on the STD algorithm. In poolHiTS, we implement a limit on the number of compounds that can be mixed in a single assay. In addition, we show that the STDbased pooling strategy is limited in the errorcorrection that it can achieve. Due to the mixing constraint, we show that it is more efficient to split a large library into smaller blocks of compounds, which are then tested using an optimized strategy repeated for each block. We package the optimal block selection algorithm into poolHiTS. The MATLAB codes for the poolHiTS algorithm and the corresponding decoding strategy are also provided.
Conclusion
We have produced a practical version of STD algorithm for pooled drug screens. This pooling strategy provides both assay compression and errorcorrection capabilities that can both accelerate and reduce the overall cost of HTS in drug discovery.
Background
Advances in automation and miniaturization of experiments and the development of reliable biological assays have made highthroughput screening (HTS) a vital step in the drug discovery process [1–3]. HTS involves the testing of a large number of candidate molecules on a biological target to identify potential drug molecules. Detection technologies such as the scintillation proximity assay (SPA), fluorescence polarization (FP) and fluorescence resonance energy transfer (FRET) are used to identify the targetbinding activity of the compounds [4–6]. Screening more than 100,000 compounds a day on a biological target has become routine, but largely through increases in assay automation via liquid handling robots and assay parallelization [7, 8]. In these assays, the most common practice is to individually test each molecule against a standardized target. Usually, only a small fraction of a compound library shows activity, while the majority of the compounds show no activity. Unfortunately, because each compound is tested only once, the presence of experimental errors (particularly false negative errors) requires substaintal efforts to validate most HTS results [9]. Due to the large size of these chemical libraries (> 50, 000 compounds) replicate screening is prohibitive. Therefore an arbitrary number of active compounds (hits) are usually chosen for secondary screening to identify inactive compounds that erroneously passed through the primary screen [10]. Disappointingly, active compounds that were missed due to false negative results in the primary assay cannot be identified from this approach and are therefore lost.
One method to reduce the sensitivity of an assay to false postitive and false negative results is pooling. In a pooling design, each compound is tested multiple times in combination with other compounds. Since very few compounds in a library are active in the assay, pooling effectively provides internal replicate measurements to confirm compound activity. However, constructing efficient pooling designs is difficult, as one would ideally like to guarentee correct identification of a known number of active compounds while correcting for random experimental errors. The general problem of pooling designs has been well studied and is described elsewhere [11].
In 2006, a novel pooling design method called shifted transversal design (STD) was introduced for biological assay design [12]. STD is based on the dual objectives of (1) minimizing the number of times any two compounds appear together in a test and (2) maintaining the pool sizes roughly equal. When compared to other pooling designs, STD provides similar or better performance in nearly every area. In this paper, we introduce a pooling strategy called poolHiTS, based on the STD algorithm, specific for the purposes of drug screening. First, we prove a limit to the errorcorrecting capacity of STDbased pooling strategies. This limit is important for drug screening as it dictates the error limit in an assay that can be used with a pooling design of this kind. Second, we modifiy the pooling design algorithm to limit the number of drugs tested in each assay, thereby enforcing a realistic experimental constraint of HTS. Third, we introduce a block design method that both simplifies and improves the assay design.
Results and Discussion
Preliminaries – STDbased pooling strategy
Algorithm 1:
 1.
Choose a prime number q, with q <n. Start with the smallest prime, 2.
 2.
Find the compression power, Γ = min{γq^{γ+1}≥ n}, therefore $\Gamma =\lceil \frac{\mathrm{log}\phantom{\rule{0.1em}{0ex}}n}{\mathrm{log}\phantom{\rule{0.1em}{0ex}}q}\rceil 1$. Set k = d Γ + 2E + 1.
 3.
Check if this choice of q and k satisfy the guarantee requirements of identifying d active compounds and correcting E errors, using the inequality, k ≤ q + 1.
 4.
If the inequality is satisfied continue to step 5, else choose the next prime in step 1 and repeat steps 2 and 3.
 5.
Once the smallest prime number q_{min} and its corresponding compression power Γ_{max} are found, all q > q_{min} will satisfy the inequality in step 3. Therefore, cycle through the values of Γ in {1, ..., Γ_{max}} to find the corresponding q. For each Γ find the smallest q that satisfies, q ≥ n^{1/Γ +1}.
 6.
Calculate the number of tests (t) needed by each q and k pair from, t = q × k.
 7.
Choose the q and k pair producing the least number of tests.
 8.
Design the pooling matrix, M = STD(n; q; k).
A detailed description of the procedure used to construct the t × n, 0–1 matrix M can be found in the original paper [12] and is reproduced in the Methods section (Construction 1) of this paper.
 1.
A compound present in at least E + 1 negative tests is tagged inactive.
 2.
A compound present in at least E + 1 positive tests, in which all other compounds have been tagged inactive, is tagged active.
Note that each compound is present in d Γ + 2E + 1 tests and no two compounds are mixed together more than Γ times. A more elaborate decoding algorithm designed to handle the presence of largerthandesignedfor values of d and E is provided in the original paper and its MATLAB implementation is provided [see Additional file 1].
Error rate
Algorithm 2: Errorrate based STD strategy
Inputs – n, d and e
 3.
If d Γ ≤ q, calculate ${E}_{\mathrm{max}\phantom{\rule{0.1em}{0ex}}}=\lfloor \frac{qd\Gamma}{2}\rfloor $, the maximum number of errors that can be corrected by this choice of q, for the given n and d.
 4.
Check if the input errorrate e is achievable using the inequality, $e\le \frac{{E}_{\mathrm{max}\phantom{\rule{0.1em}{0ex}}}}{q(d\Gamma +2{E}_{\mathrm{max}\phantom{\rule{0.1em}{0ex}}}+1)}\times 100$. If e is achievable then continue to step 5, else go back to step 1 and try the next q.
 5.
Cycle through all values for E from 0 to E_{max} to find the minimum E (E_{min}) that satisfies the inequality in step 4. Use E_{min} to calculate, k = d Γ + 2E_{min} + 1 and the number of tests, t = qk.
 6.
Similar to Algorithm 1, cycle through the values of Γ smaller than the Γ_{max} to find the corresponding q and hence t.
 7.
Use the design parameters (q and k) that need the minimum number of tests.
 8.
Construct the pooling design, M = STD(n; q; k), as usual.
Mixing Constraint
 1.
Each compound must be present at a sufficiently high concentration so as to be detectable by the assay within a physiologically reasonable range.
 2.
The total ionic strength of the test solution must be low enough to prevent precipitation of compounds or possible changes to the biological target.
 3.
The assay must be reasonably simple to physically construct.
From these constraints we can conclude that the total number of drugs that can be practically included in any experimental test well is relatively small. For example, for a relevant screening concentration of ~10 μ M, we can assume that each well can have ~10 compounds mixed in it. If more drugs are mixed, the cost of creating the assay increases and the ionic strength of the well mixture may become too high, resulting in inaccurate screening results. This limit can be specified as an input parameter to the pooling design, based on the highthroughput assay being implemented.
Here we extend the STDbased pooling strategy to introduce an explicit mixing constraint. Let m be the mixing constraint, defined as the maximum number of compounds mixed in a well of the pooling scheme. Normally the STD design mixes at most $\lceil \frac{n}{q}\rceil $ compounds in each well [12]. However, this feature can break down when the input values of n, d and e (hence E_{min}) are such that, for a certain choice of q and k (= d Γ + 2E_{min} + 1), k = q + 1 and $\lceil \frac{n1}{{q}^{\Gamma}}\rceil $ <q  1, resulting in an unusually large number of compounds mixed in some tests and some tests with no compounds in them. The original STD construction can be easily modified by removing tests with no compounds in them. The details of the correction are provided in Proof 2 of the Methods section. Having made this correction, we can implement the mixing constraint as follows [see Additional file 2].
Algorithm 3: Mixing constraint implementation
Inputs – n, d, e and m
 6.
Using the q and k values obtained so far in the algorithm to choose one of the following options.
 a.
If k <q + 1 and $\lceil \frac{n}{q}\rceil $ ≤ m, use Construction 1 (Methods section) requiring t = qk tests.
 b.
If k = q + 1, n = q^{Γ+1}  1 and q^{Γ} ≤ m, use Construction 1 requiring t = q(q + 1) tests.
 c.
If k = q + 1, $\lceil \frac{n1}{{q}^{\Gamma}}\rceil $ <q  1 and q^{Γ} ≤ m, use Construction 2 (Methods section) requiring t = q^{2} + $\lceil \frac{n1}{{q}^{\Gamma}}\rceil $ + 1 tests.
 7.
Similar to the previous algorithms, cycle through the values of Γ smaller than Γ_{max} to find the corresponding q and hence t.
 8.
Use the design parameters (q and k) that require the minimum number of tests.
Repeated Blocks
Limiting the number of compounds that can be mixed in a pooling strategy reduces the savings in tests that could otherwise have been obtained. For example, without the mixing constraint, screening a library of 10,000 compounds with 3 expected actives and no error requires only 110 tests, using STD(10000; 11; 10), each mixing 910 (=$\lceil \frac{10000}{11}\rceil $) compounds in them. However, a mixing constraint of 10 compounds per test increases the required tests to 4036, using STD(10000; 1009; 4). Now, consider the STDbased strategy for 400 compounds while expecting only 1 active compound in a similar errorfree setting and a mixing constraint of 10 compounds per well. The number of tests needed in this case is 82, using the STD(400; 41; 2) design. If we now divide the original 10, 000 compound library into 25 blocks of 400 compounds each and used the STD(400; 41; 2) design repeated on those 25 blocks, the total number of tests is only 25 × 82 = 2050, almost half the original requirement! The tradeoff here is that the block assay design guarantees the detection of only 1 active compound out of every 400 compounds tested. However, given that we expected only 3 active compounds in a library of 10, 000 compounds, this implies that there is ~99.5% chance of finding, at best, 1 active compound among the 400 randomly selected compounds for a block (using the hypergeometric distribution). This block decomposition algorithm is inspired by a form of pooling used in NMR screening [14]. This example demonstrates that we can make an informed choice about a block size that can, not only, help reduce the number of tests needed while enforcing the mixing constraint but the smaller block size also implies that a better errorrate can be handled by the design (as seen in Figure 3).
Based on this choice of d_{ B }, a STDbased pooling design of size t_{ B }× n_{ B }can be generated using Algorithm 3. The number of tests needed for the whole library would be B × t_{ B }.
poolHiTS
We now define poolHiTS as an STDbased pooling strategy which takes in as input the compound library size (n), maximum number of active compounds expected (d), maximum errorrate expected (e), mixing constraint (m), and design confidence metric (p_{ b }). poolHiTS(n, d, e, m, p_{ b }) produces a t × n mixing matrix M, which guarantees the success of the pooling scheme for the given input parameters. The algorithm for poolHiTS, which includes errorrate specification, mixing constraints, and optimal block size selection, is as follows. The MATLAB implementation of the poolHiTS algorithm is provided [see Additional file 3].
Algorithm 4: poolHiTS algorithm
 1.
Choose a value of d_{ B }in {1, ...,d  1}.
 2.
Find the set of n_{ B }that satisfy the inequality in Equation 2.
 3.
For each n_{ B }in this set use Algorithm 3 to evaluate STD(n_{ B }; q_{ B }; k_{ B }), if it exists, for the given e and m. Calculate the total number of tests needed from B × t_{ B }, $B=\lceil \frac{n}{{n}_{B}}\rceil $.
 4.
Choose the next value of d_{ B }and repeat steps 2 and 3.
 5.
After testing all values of d_{ B }in {1, ...,d  1} and the corresponding n_{ B }, select the d_{ B }, n_{ B }pair that require the least number of tests. The whole library design (which corresponds to n_{ B }= n, d_{ B }= d and B = 1) should also be included while making this choice.
 6.
Design the pooling matrix, M = STD(n; q; k), for the choice of q and k.
Block Design Example
Number of blocks (B)  Number of compounds per block (n_{ B })  Number of active compounds expected per block (d_{ B })  Number of tests needed per block (t_{ B })  Actual errorrate handled (e)  Actual mixing constraint (m)  Total number of tests needed (t) 

1  10000  3         
25  400  1  492  1.02%  10  12300 
50  200  1  92  1.09%  9  4600 
77  130  1  52  1.92%  10  4004 
91  110  1  44  2.27%  10  4004 
100  100  1  44  2.27%  10  4400 
500  20  1  20  5%  4  10000 
Conclusion
In this paper, we present a pooling strategy called poolHiTS which implements tailored modifications and enhancements that make the shifted transversal design (STD) algorithm appropriate for drug discovery. First, we demonstrate how to switch from specifying the number of errors (E) for a STDbased strategy to an error rate (e), which is the percentage of errors expected in tests. We show that there is an upper limit to the errorrate that can be handled by this STDbased algorithm and this error limit (Equation 1) strongly constrains pooling designs to less noisy screening assays. We implement error rate as an input experimental parameter via Algorithm 2.
Second, we introduce and implement explicit mixing constraints to make pooling significantly more relevant to HTS assays via Algorithm 3. We also provide a necessary correction to the STD construction algorithm pertinent to the mixing constraint.
Third, we introduce the concept of repeated block designs that retains the efficiency of pooling strategies in the face of the mixing constraint and errorrate limitations. We show that by using this block design, we are able to simplify the assay construction, increase the error tolerance and decrease the assay size. The combination of these features produce the poolHiTS strategy described in Algorithm 4. The MATLAB implementations of the poolHiTS algorithm and an example of its use are provided in the Additional Files section. poolHiTS provides a promising route to both reducing the cost and increasing the accuracy of high throughput drug screening. Although poolHiTS primarily focuses on drug screening, these same design methods can apply equally well to other screening environments where the number of perturbations to a system is finite or small (mixing constraint) and the error rate of the assay is approximately known.
Methods
Construction 1
Given the design parameters q and k, the pooling matrix M = STD(n; q; k) can be constructed as follows. The STD has a layered construction consisting of k layers of q × n boolean matrices. For all j ∈ {0, ..., k  1}, let M_{ j }be a q × n boolean matrix representing layer L(j), with columns C_{j,0}, ...,C_{j,n1}.
Let the circular shift operator, σ_{ q }, be defined as, ∀(x_{1}, ..., x_{ q }) ∈ {0,1}^{ q }, ${\sigma}_{q}\left[\begin{array}{c}{x}_{1}\\ {x}_{2}\\ \vdots \\ {x}_{q}\end{array}\right]=\left[\begin{array}{c}{x}_{q}\\ {x}_{1}\\ \vdots \\ {x}_{q1}\end{array}\right]$ and ${C}_{0,0}=\left[\begin{array}{c}1\\ 0\\ \vdots \\ 0\end{array}\right]$. Note that σ_{ q }is a cyclic function and when applied q times maps {0, 1}^{ q }onto itself, ${\sigma}_{q}^{s}\left[\begin{array}{c}{x}_{1}\\ {x}_{2}\\ \vdots \\ {x}_{q}\end{array}\right]=\left[\begin{array}{c}{x}_{1}\\ {x}_{2}\\ \vdots \\ {x}_{q}\end{array}\right]$, s = q. To design a layer L(j), for all i ∈ {0, ..., n  1} construct ${C}_{j,i}={\sigma}_{q}^{s(i,j)}{C}_{0,0}$ where,

if j <q: $s(i,j)={\displaystyle \sum _{c=0}^{\Gamma}{j}^{c}\lfloor \frac{i}{{q}^{c}}\rfloor}$

if j = q: $s(i,q)=\lfloor \frac{i}{{q}^{\Gamma}}\rfloor $
The layers L(j) are put together to form M by, $\text{STD}(n;q;k)={\displaystyle \underset{j=0}{\overset{k1}{\cup}}L(j)}$. A detailed description of the construction is available in the original STD paper [12].
Proof 1: Errorrate limit
Using the above inequality it is possible to deduce the maximum errorrate that STD can handle for a particular ncompound library with at most d active compounds and an assay with a limit on mixing not more than m compounds in a test. However, in most cases, the numerical value of the maximum error rate can only be calculated by executing (Algorithm 3) completely. For the simplest case of d = 0, since q is a prime number and hence odd (except for q = 2), the term $\lfloor \frac{qd\Gamma}{2}\rfloor $ is reduced to $\lfloor \frac{q}{2}\rfloor $ and is always equal to $\frac{q1}{2}$. Simplifying the expression for the error limit, we get, $e\le \frac{q1}{2{q}^{2}}\times 100$. Whereas, for q = 2 (the only even prime number), the value of the limit is, e ≤ $\frac{1}{6}$ × 100 ~ 16.67%. This is the maximum errorrate that STDbased designs can guarantee correction for.
Proof 2: STD Correction
In the case where the choice of the design parameters q and k (= d Γ + 2E_{min} + 1) are such that, k = q + 1, then the construction of the last layer (j = k  1, k = q + 1) will use s(i, q) = $\lfloor \frac{i}{{q}^{\Gamma}}\rfloor $ (see Construction 1).
When the library size n is such that $\lceil \frac{n1}{{q}^{\Gamma}}\rceil $, only rows from 0 to $\lceil \frac{n1}{{q}^{\Gamma}}\rceil $ have compounds in them and the rest of the rows in the layer are empty. It follows that in this layer the number of compounds mixed in each test is q^{Γ}. Usually, the number of compounds per test is $\lceil \frac{n}{q}\rceil $ and in this case we know that $\frac{n}{q}<{q}^{\Gamma}$. Hence, it is essential to make this correction in order to correctly implement the mixing constraint. For example, In Table 2 of the original STD paper [12], there is a case where n = 10000, d = 3 and E = 2 with the optimal choice of q = 13 and k = 14. In this case k = q + 1, so the actual number of tests is t = 174, and not 182 (= 13 × 14) as stated in the paper, and the maximum number of compounds being mixed is actually 2197 rather than 769.
Construction 2
A modification is required for the special case where the choice of design parameters (q and k) is such that k = q + 1 and $\lceil \frac{n1}{{q}^{\Gamma}}\rceil $ <q  1. The construction remains essentially the same as Construction 1 with the only modification being the removal of the unnecessary tests with no compounds in them. As shown in Proof 2, the last q  ($\lceil \frac{n1}{{q}^{\Gamma}}\rceil $ + 1) rows of M need to be removed. It should be noted that the maximum number of compounds being mixed in a test is q^{Γ}. The MATLAB implementation of this construction is provided [see Additional file 5].
Proof 3: Choice of Block Size
The identification of the experiment design parameter d_{ B }for the block requires the specification of a probability (p_{ B }) representing a confidence in finding at most d_{ B }active compounds in a block of size n_{ B }, which is the sum of all the individual probabilities of finding exactly i = {0, 1, ..., d_{ B }} active compounds out of n_{ B }compounds. Therefore the condition for calculating d_{ B }is $\sum _{i=0}^{{d}_{B}}P(i)\ge {p}_{B}$, thus arriving at Equation 2. It is useful to note that other design parameters of errorrate (e) and mixing constraint (m) are so defined, as to be the same for individual blocks and the whole library. The MATLAB implementation of this equation is provided [see Additional file 6].
Declarations
Acknowledgements
Work funded by the University of Michigan and NIH 1 U54 DA02151901A1 and NIH 1 P20 HG00389001. We would also like to thank the anonymous reviewers for valuable comments regarding the presentation of the results.
Authors’ Affiliations
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