History of the NCI-60 screen and COMPARE algorithm

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Introduction to the NCI-60 screen and COMPARE algorithms

Prediction of Biochemical Mechanism of Action from the In Vitro Antitumor Screen of the National Cancer Institute

Kenneth D. Paull, Ernest Hamel, and Louis Malspeis

We are in an age when experimental data can be analyzed with unprecedented ease and speed. This makes entirely new approaches to large accumulations of data possible. The new National Cancer Institute (NCI) in vitro anticancer screening program generates prodigious quantities of information-laden biological test results, captured in an easily accessible computerized database. We describe an analysis of the NCI in vitro database that answers questions that could not be asked in any previous screening program.

The routine implementation of the current NCI in vitro anticancer screen to evaluate the efficacy of synthetic compounds and natural products was initiated in April 1990. The screen, developed over a period of several years (1, 2, 3) by the Developmental Therapeutics Program (DTP), employs 60 human tumor cell lines that have been grouped in disease subpanels including leukemia, non-small-cell lung, small-cell lung, central nervous system, colon, melanoma, ovarian, and renal tumors or cell lines. In December 1993, several changes were made to this panel of cell lines. A new panel of eight breast lines and a new panel of two prostate lines were added. To keep the panel total at 60, 10 lines were identified as redundant or technically difficult to use (e.g., the two small cell lung lines) and were dropped. Approximately 1000 compounds and natural product extracts have been screened each month. The decision that there would be no alteration of the assay method and cell lines employed in the screen for more than 12 months has permitted us to evaluate thousands of compounds under identical conditions.

Our analysis of these data is performed by a program we call COMPARE (2,3,4,5,6,7,8). A probe or “seed” compound can be specified by using the compound’s NCI accession number (the NSC number). The COMPARE algorithm then proceeds to rank an entire database in the order of the similarity of the responses of the 60 cell lines to the compounds in the database to the responses of the cell lines to the seed compound. Similarity of pattern to that of the seed is expressed quantitatively as a Pearson correlation coefficient (PCC). The results obtained with the COMPARE algorithm indicate that compounds high in this ranking may possess a mechanism of action similar to that of the seed compound (6, 7).

Screening Procedures

The NCI screening procedures were described (9) as were the origins and processing of the cell lines (9,10,11,12). Briefly, cell suspensions that were diluted according to the particular cell type and the expected target cell density (5000-40,000 cells per well based on cell growth characteristics) were added by pipet (100 µL) into 96-well microtiter plates. Inoculates were allowed a preincubation period of 24 h at 37° C for stabilization. Dilutions at twice the intended test concentration were added at time zero in 100-µL aliquots to the microtiter plate wells. Usually, test compounds were evaluated at five 10-fold dilutions. In routine testing, the highest well concentration is 10E-4 M, but for the standard agents the highest well concentration used depended on the agent. Incubations lasted for 48 h in 5% CO2 atmosphere and 100% humidity. The cells were assayed by using the sulforhodamine B assay (13, 14). A plate reader was used to read the optical densities, and a microcomputer processed the optical densities into the special concentration parameters defined later.

Special Concentration Paramaters

The NCI renamed the IC50 value, the concentration that causes 50% growth inhibition, the GI50 value to emphasize the correction for the cell count at time zero; thus, GI50 is the concentration of test drug where 100 × (T - T0)/(C - T0) = 50 (3,9)). The optical density of the test well after a 48-h period of exposure to test drug is T, the optical density at time zero is T0, and the control optical density is C. The “50” is called the GI50PRCNT, a T/C-like parameter that can have values from +100 to -100. The GI50 measures the growth inhibitory power of the test agent. The TGI is the concentration of test drug where 100 × (T - T0)/(C - T0) = 0. Thus, the TGI signifies a cytostatic effect. The LC50, which signifies a cytotoxic effect, is the concentration of drug where 100 × (T - T0)/T0 = -50. The control optical density is not used in the calculation of LC50.

These concentration parameters are interpolated values. One uses the concentrations giving GI50PRCNT values above and below the reference values (e.g., 50 for GI50) to make interpolations on the concentration axis. Currently, about 45% of the GI50 records in the database are “approximated”. In 42% of the records, the GI50PRCNT for a given cell line does not go to 50 or below. For mean graph (see the discussion later) and COMPARE purposes, the value assumed for the GI50 in such a case is the highest concentration tested (HICONC). Similar approximations are made when the GI50 cannot be calculated because the GI50PRCNT does not go as high as 50 or above (3% of total). In this case, the lowest concentration tested is used for the GI50. Corresponding approximations are made for the TGI and for the LC50.

We use these “approximated” GI50 (TGI and LC50) values in the mean graph and in COMPARE because they represent valued information even though the information is less exact than the measured values would be if the measured values were available. In an extreme case where a compound is essentially inert and the GI50s are all represented by the HICONC approximation, the mean graph becomes a flat vertical line (the mean line) and COMPARE has no pattern to correlate. The opposite extreme case is where a compound is so potent that the lowest concentration tested is used to approximate all of the GI50s. In this case, the mean graph is also a flat vertical line and COMPARE has nothing to correlate. The difference in the two extreme cases is in the retests that are done. The inert compound would not be retested. The potent compound would be retested at a more appropriate concentration range.

Between the two extremes are examples with few or many approximated GI50s. These can give good results in COMPARE, but the presence of the approximated GI50 requires an additional strategy in the database preparation. The strategy is to treat the data for a given compound in groups defined by the range of concentrations used in the experiment. These ranges are conveniently labeled according to the HICONC. Thus, if multiple tests of a compound are present in the database, only those experiments with the same HICONC are averaged.

This strategy results in compounds having more than one entry in the database. There are differences in the “approximated” GI50 content of the averaged data, and the averages are calculated from different experiments. Therefore, one should expect that the COMPARE-generated correlation coefficients may be different for the same compound tested at different HICONCs. Moreover, at run time the COMPARE user has the option to choose any one of the HICONC sets for the probe pattern averaging, or the user may choose to average all seed data regardless of the HICONC. The consequences of these options and strategies will be apparent later in the examples provided under Applications of COMPARE. For instance, the probe may find itself several times in the COMPARE list at less than 1.00 correlation coefficient.

The Mean Graph

The discussion of COMPARE presented in this chapter requires an understanding of the mean graph, a means of presenting the in vitro test results developed by the staff of DTP (2, 4, 5) to emphasize differential effects of test compounds on various human tumor cell lines.

A “mean graph” is a pattern created by plotting positive and negative values generated from a set of GI50, TGI, or LC50 values. The positive and negative values are plotted along a vertical line that represents the mean response of all the cell lines in the panel to the test agent. Positive values project to the right of the vertical line and represent cellular sensitivities to the test agent that exceed the mean. Negative values project to the left and represent cell line sensitivities to the test agent that are less than the average value.

The positive and negative values, called deltas, are generated from the GI50 data (or TGI or LC50 data) by a three-step calculation. The GI50 value for each cell line tested against a test compound is converted to its log10 GI50 value. These log10 GI50 values are averaged. Each log10 GI50 value is subtracted from the average to create the delta. Thus, a bar projecting 3 units to the right denotes that the GI50 (or TGI or LC50) for that cell line occurs at a concentration 1000 times less than the average concentration required for all the cell lines used in the experiment.
The complete presentation and organization of the mean graph data were intended to optimize subpanel specific effects, for the listing of cell lines is by disease type, but this presentation of the data is only incidental to the COMPARE concept.

Experience with a wide variety of test compounds has led to the conclusion that a presentation of the mean graphs at all three special concentrations, the GI50, TGI, and LC50, is most useful (3).

The COMPARE Algorithm

COMPARE analyses are rank-ordered lists of compounds. Every compound from one of several specially prepared databases is ranked for similarity of its in vitro cell growth pattern to the in vitro cell growth pattern of a selected seed or probe compound. To derive COMPARE rankings, a scaler index of similarity between the seed compound cell growth pattern and the pattern for each of the COMPARE database compounds must be created. Two indexes of similarity, the average difference between deltas and the correlation coefficient, are described later, but others are possible (8). The average difference method (ADM) was developed and reported (4) before the correlation coefficient method (CCM), and the database preparation procedure for the ADM gives a database usable by either method. Therefore, the databases described later are those required by the ADM. The CCM can use these databases, but the mathematical characteristics of this method make the computation of deltas unnecessary.

Individual cell growth patterns can be represented by delta values. The number that is depicted in the mean graph as the length and direction of a bar on the graph is called delta. The ADM utilizes the same description of in vitro data as the mean graph.

Building the COMPARE Database

To facilitate routine use of COMPARE, several types of COMPARE databases are precalculated with values and stored in SAS data sets (SAS Institute, Inc.). These COMPARE databases are automatically updated (usually each week) as additional compounds are tested. The following detailed description illustrates the processes for collecting data for an ADM database.

The main Oracle database table that contains the GI50 data of the in vitro anticancer screen was searched by Structured Query language (SQL) (the search language for Oracle and many other relational database management systems, SQL). The resulting file was output as an SAS data set ready for further processing by SAS programs. Each test was represented in the SAS data set by six variables: NSC, Panelnbr, Cellnbr, HICONC, GI50, and Discreet status. The NSC identifies which compound was tested. The Panelnbr and Cellnbr together describe the cell line used in the test. HICONC defines the highest dose used in the five-dose assay. The Discrete status denotes whether the compound is confidential or not. The log10 of the GI50 was taken. The database was then sorted by NSC, HICONC, and cell line (Panelnbr and Cellnbr), and the log10 GI50 values were averaged for the same NSC, HICONC, PANELNBR, and CELLNBR. The averaged log10GI50 were named M_GI50. For preparation of a database suitable for the CCM (but not the ADM), it is only necessary to sort the database at this stage by cell line (e.g., by Panelnbr and Cellnbr). The database would then be ready for use. 

For preparation of a database suitable for both the ADM and the CCM, deltas have to be computed. For each NSC at a particular HICONC, the means of the M_GI50 values were calculated and named MeanGI50. By subtracting the M_GI50 from the corresponding MeanGI50, the deltas were calculated. As a quality-control measure, sets of cell line deltas for a compound where there were fewer than 35 delta values were excluded. The number 35 was arbitrary, but experience suggested that sets with too few delta values sometimes gave spurious COMPARE matches. It would certainly be better to determine the cutoff value statistically, but this has not been done. Finally, for preparation of this data set for the eventual merging with data sets derived from seed data, this data set was sorted by cell line order (Panelnbr and Cellnbr). The database was ready for use.

Using COMPARE 

To run COMPARE analyses, one must first select from a menu a COMPARE program appropriate to the analysis desired. The selection determines which database will be analyzed. The current options are the standard agent database with 171 compounds, the synthetic compound database with >40,000 compounds and growing (this includes synthetic compounds and natural products of known structure), the natural product crude extract database with >20,000 screened extracts and growing, and other special-purpose databases. 

The selection also determines which method of comparison will be used (CCM or ADM), what type of seed may be entered (standard agent or any screened compound or extract), and the level of the analysis (GI50, TGI, LC50, or all three simultaneously). The analyst then enters the NSC number of the desired seed compound and decides if all, some, or just one of the experiments performed on the seed should be averaged. Once the choices are made, the analyst executes the program. The execution begins with the collection, at the time of analysis, of the seed data containing GI50 (or TGI or LC50 or all three) values directly from the master Oracle database. The seed data collected from the master Oracle database is converted to an SAS data set. If more than one experiment was collected for the seed, the data are then averaged as described previously for building the database. For the ADM, the log10GI50 values from the seed must be converted to a set of delta values just as deltas were calculated for the COMPARE database. 

In the next step, pairs of delta values are created (by using an SAS MERGE data step). Each pair consists of the delta value from the seed for a particular cell line and the delta value of a database compound for the same cell line. For example, the delta value calculated for HL-60 data from the seed is paired with the HL-60 delta value calculated for each database compound. For inclusion in the similarity index calculation, both methods (ADM and CCM) require that the delta value (or optionally the M_GI50 for the CCM) be present in both the seed and the database compound. Thus, if HL-60 was not successfully tested against the seed, no use would be made of any HL-60 data present in the database compounds. If HL-60 data are available for the seed, they will be used only in those cases where HL-60 data are also present in the database compounds. Thus, the seed data determine the maximum number of pairs of delta values that will be used to calculate the index of similarity of each database compound. Variations in the number of cell lines tested against individual database compounds will determine if the number of pairs in the seed data or some lesser number of pairs is used for the particular similarity index calculations.

Average Difference Method (Note, January 2025, this method is no longer implemented as part of the current COMPARE tools.  It is described and discussed here for completeness.

The first step in calculating this index of similarity is to take the difference between paired deltas. An average of these differences, by compound, is computed for each compound. The compounds are sorted by their average difference. The compound with the smallest average difference is the most similar to the seed compound. 

Correlation Coefficient Method 

A pairwise correlation coefficient ( PCC ) with the seed is calculated for each compound in the database. Those compounds with the highest correlation coefficient are most similar to the seed. 
We use a commercial statistical package procedure (the SAS procedure PROC CORR) to obtain PCCs. The PCC provides an excellent index of similarity as judged by the many successful examples provided under Standard Agents and the Standard Agent Database, but other types of correlation coefficients could potentially work as well. 
Analysts are usually interested in finding only those compounds in the database that are most similar to the seed. Thus, the list is truncated to 100 of the most similar compounds. Common chemical names, if they are on file, are added automatically to the truncated answer list. 

Surprisingly similar, but totally independent, work (97,98) was published essentially concurrently with the early publications of mean graph and COMPARE in an entirely different area of application.

Early Work based on the NCI-60 screening results and the COMPARE tools.

Discovery of Novel Compounds with a Particular Mechanism

COMPARE has become an integral part of the NCI's evaluation of newly screened compounds. Even though the mean graph of every compound tested by the NCI is inspected by eye, most newly screened compounds do not show sufficient pattern to warrant a COMPARE analysis. Those that exhibit a detectable pattern are analyzed with a version of the COMPARE program that uses the standard agent database. This database is small enough to permit an interactive analysis. Thus, it can be determined in a matter of seconds whether a compound acts by a mechanism of action similar to that of one of the standard agents.

Alternately, because compounds that affect specific molecular targets produce distinctive mean graph patterns, one can use COMPARE to search for new compounds heretofore unknown to have that action. Any compound can be used as a seed to search among all the agents that have been screened in the synthetic compound database for those that may influence the same target as the seed. For definitive confirmation of mechanism, laboratory studies would be required. Further, the seed can also be used to search the natural product extract database to identify crude extracts that may contain compounds affecting the target.

Introduction to NCI-60 and COMPARE tools

The results obtained by analyzing standard agent patterns with the COMPARE algorithm strongly indicate that these patterns often reflect the mechanism of action by which chemical substances act upon cells in vitro. As a consequence, COMPARE can be used to help achieve important goals of the DTP in vitro antitumor drug screening program, such as identification of newly screened compounds for referral for in vivo antitumor testing in xenograft models.

In the following applications, COMPARE was used to readily provide information never before available to screening programs. With a single COMPARE analysis, requiring approximately 45 s to perform, one can obtain a good estimate as to whether a newly screened chemical acts by a previously recognized antitumor mechanism. The “goodness” of this estimate has not been statistically validated; rather, it is supported by the excellent results obtained with standard agents.

Lead optimization, the type of drug development where medicinal and organic chemists make small modifications to lead structures to improve drug activity, reduce toxicity, and alter other properties, has been an important activity for many research groups and a productive source of clinical drug candidates. COMPARE has the potential to serve a very useful role in this type of analogue development because it can readily determine if a particular new analogue acts by the target mechanism. Numerous structural analogues have been identified by COMPARE as acting by the expected mechanism and referred for in vivo testing.

Inasmuch as COMPARE does not use chemical structure data to select compounds that act by related mechanisms, it can detect structurally novel compounds as readily as analogues provided they act by the same mechanism. COMPARE can thus help identify classes of compounds never before recognized as topoisomerase I or II agents, tubulin-binding agents, antimetabolites of various types, and so on.

For example, numerous newly screened chemicals have been identified with COMPARE as independent proof of the tubulin-binding activity was obtained by published laboratory procedures (6,7). Many of these compounds had structures that were not analogous to those of previously identified antimitotic drugs. Once a new structural class of known mechanism of action is discovered, then additional screening of available analogues and synthesis of additional analogues can be used to define optimal structural features for antitumor activity.

Given that the mechanistic determinations of COMPARE have been validated in these cases by using standard laboratory procedures, it is considered appropriate to use COMPARE determinations, without further laboratory tests for proof, as a basis for requesting in vivo tests of in vitro screened chemicals as part of a continuing screening program evaluation.

Another use of COMPARE is for the analysis of mean graph data of compounds with significant in vivo activity but about which little is known regarding the mechanism of action. Many such compounds have not been tested in the clinic so they are not among the standard agents. It is reasonable to conduct in vivo evaluation on COMPARE matches to these types of undeveloped lead compounds, assuming that similarity of cytotoxicity patterns documented with COMPARE indicates analogous, although unknown, mechanism of action.

A major goal of the new DTP anticancer screening program is to identify compounds with disease subpanel specificity. This means that a compound is significantly more toxic to cells of one, two, or three particular histological subcategories (e.g., colon, renal, melanoma, etc.) than it is to the remainder of the tumor cell panel. A compound with significant subpanel specific toxicity may be of interest without regard to its mechanism of action. Its novelty may even be more interesting if it does not share a mechanism of action with any known agent, and this too can be evaluated by using COMPARE.

Supplemental Materials for COMPARE

Antitubulin agents

Along with the standard agent database, the COMPARE algorithm had been particularly effective with antimitotic agents directed against tubulin. The top 10 matches with each known antimitotic agent in the database included all other such agents in the database. Consequently, in 1990 when only a few thousand compounds had passed through the screen, we began a study to determine whether COMPARE could select other antimitotic agents that had been examined for cytotoxicity against the 60 cell lines and whether entirely novel antimitotic agents could be identified. We knew that such agents included colchicine, dolastatin 10 (46,53), dolastatin 15 (70), combretastatin A-4 (71, 72), podophyllotoxin, carbamates (73), and benzylbenzodioxole derivatives (74). The entire database was probed at that time with colchicine, vinblastine, vincristine, podophyllotoxin, and paclitaxel, and we arbitrarily examined the best 100 matches with each. We found that no matter what antimitotic agent was used as the seed, whether one of those in the standard agent database (vinblastine, vincristine, or paclitaxel) or one of those that had been separately screened (colchicine or podophyllotoxin), most known antimitotic compounds in the overall database appeared among the top 100 matches.

In this initial work, we noted two potently cytotoxic marine natural products that appeared repeatedly. These were the complex macrolide polyethers halichondrin B and homohalichondrin B (75,76). These natural products were specifically examined for interactions with tubulin and for antimitotic activity, and such properties were confirmed (6). This work demonstrated that the halichondrin B noncompetitively inhibited the binding of vinblastine to tubulin, and thus these agents may bind at a unique site on the protein.

This success led us to examine compounds on the five lists with structures we considered novel for antimitotic agents for effects on in vitro tubulin polymerization (7). Positive and negative compounds in this assay were then evaluated in terms of their cytotoxicity with the human tumor cell lines and in terms of their PCC values relative to the five seeds. This analysis led us to conclude that the COMPARE algorithm would yield optimal results with antimitotic agents if we imposed two restrictions on the compounds selected with any seed. First, the PCC should be at least 0.6. Second, these initial results indicated that compounds with low cytotoxicity generally did not greatly affect tubulin polymerization. Therefore, we imposed as a second criterion that selected compounds have a GI50 value of 1 µ M or less in the original screen with HL-60 (TB) human leukemia cells.

We then performed our “definitive” searches of the overall database in late 1990 at a time when over 7000 compounds had been screened. We used nine seeds (vinblastine, colchicine, podophyllotoxin, vincristine, paclitaxel, maytansine, dolastatin 10, rhizoxin, and combretastatin A-4). Beside the seeds themselves, 73 compounds in the database met the criteria summarized previously. Among these compounds were 13 analogues of podophyllotoxin, 3 of colchicine, 9 of dolastatin 10, 7 of combretastatin A-4, 3 of paclitaxel, 3 of carbamates, and 2 of benzylbenzodioxole derivatives. There were 32 structurally novel compounds representing 19 distinct chemical species. Two of these were halichondrin B and homohalichondrin B, which had been identified by seven of the seeds. Because halichondrin B may have a unique binding site on tubulin (6), we used it as well as a seed for COMPARE but no additional compounds were selected from the database.

The antitubulin and antimitotic properties of the other 30 compounds, including their structures, have been presented in detail elsewhere (7). In summary, 20 compounds (representing 11 distinct chemical species) were effective inhibitors of tubulin polymerization and caused the accumulation of cells arrested in mitosis in tissue culture. These were all synthetic compounds with relatively simple chemical structures, and they all interfered with the binding of colchicine to tubulin. All but one of these compounds were identified by at least six seeds, and the remaining compound was identified by a single seed. One compound, identified by a single seed, weakly inhibited tubulin polymerization and colchicine binding, but we could not demonstrate accumulation of mitotic cells with this agent even though it was cytotoxic. Seven compounds, representing four chemical groups, had no affect on tubulin polymerization or accumulation of cells arrested in mitosis. Only one of these agents was identified by multiple seeds, and we were unable to confirm the cytotoxicity observed in the original screening studies. This finding suggests that this compound was chemically unstable. Finally, two compounds were particularly interesting. These two agents, one of which was tritylcysteine, were identified by multiple seeds, and they both caused the accumulation of cells arrested in mitosis in tissue culture. Neither compound interacted with tubulin in vitro. Further studies are required to determine whether microtubules are the cellular target of these two compounds or whether their target is another cellular component involved in the mitotic process.

Therefore, we conclude that we have established a reasonable scheme of using COMPARE to identify new antimitotic compounds that have a high probability of interacting with tubulin. Our overall data indicate that we could reduce the number of false positives found with COMPARE by adding a third criterion, identification by more than four seeds. We hesitate to do this, however, because the potent antimitotic natural product dolastatin 15 (70) was found with only two seeds.

Topoisomerase II agents

To search for topoisomerase II active compounds, one could use an agent with established clinical efficacy such as VP-16 or VM-26 as the seed. The best matches derived would be compounds exhibiting a pattern of tumor cell inhibition similar to that of the seed. Alternately, use of a synthetic compound as the seed has the potential to furnish a list of compounds with subtle differences in antitumor properties from those of the standard agents. Among the synthetic topoisomerase II inhibitors, there are numerous potential seeds. In the example presented here, we chose a semisynthetic demethylepipodophyllotoxin derivative only somewhat related in structure to VP-16 (77). The seed compound was one of a set of (arylamino)demethylepipodophyllotoxins that are closely related in structure (77). The synthetic compound database was searched at both the GI50 (Table XV) and TGI levels (Table XVI). At the time of the search, the database contained data for more than 20,000 compounds. At each level, only the top 40 matches from among the entire database are listed.

Among these 40 compounds at both the GI50 and TGI levels, VP-16 analogues predominate. At each level, it is noteworthy that neither VP-16 nor VM-26 appears among the top matches. The COMPARE result suggests that the pattern of cell inhibition by these analogues is different from that of VP-16.

Cheng et al. (78) have evaluated the seed compound and five of the other VP-16 analogues found by COMPARE for their activity as inhibitors of human DNA topoisomerase II in vitro and their cytotoxic efficacy against the KB cell line and its VP-16-resistant variants. As inhibitors of DNA topoisomerase II, five of these compounds were found to be 5-10-fold more potent than VP-16. Not only were the compounds cytotoxic to KB cells but also the compounds were cytotoxic to the variants that exhibited a lower DNA topoisomerase II content or overexpression of MDR1 phenotype and a decreased cellular uptake of VP-16. Thus, the absence of VP-16 from the top COMPARE matches may reflect differences in the pattern of growth inhibition of the NCI cell line panel stemming from mechanistic differences in antitumor activity by VP-16 and this group of analogues.

The only standard agents that appear in the lists are menogaril and AMSA, both DNA topoisomerase II inhibitors. At both the GI50 and TGI levels, the list was interspersed with acridine derivatives, most of which were structural relatives of AMSA. Also, at the TGI level anthracyclines and anthraquinones were found, which are structurally related to compounds well known to be topoisomerase II inhibitors.

At the TGI level, two camptothecin derivatives appeared in the list near the bottom. This is analogous to the small number of topoisomerase II inhibitors that were found at the bottom of the COMPARE list when camptothecin was employed as the seed at GI50 level. Although these results may be due to variability in the in vitro bioassay, it is also feasible that there is a mechanistic basis for the result.

In accord with any request by investigators submitting compounds to the program for testing who wish to maintain the chemical structures confidential, an illustration of examples of heretofore unknown topoisomerase II inhibitors is presently precluded. Notwithstanding, the analysis using the VP-16 analogue as the seed depicts the technique that is used for such discoveries. For selected discoveries, experimental confirmation of mechanism in the laboratory has served to stimulate the continued use of COMPARE.

Inosine monophosphate dehydrogenase inhibitors

Jayaram et al. (83,84,85) verified that benzamide riboside (BR) was an inosine monophosphate (IMP) dehydrogenase inhibitor, following demonstration by COMPARE that BR correlated well with tiazofurin and selenazofurin cytoxicity patterns. BR was identified by routine surveillance of screened output by using the standard agent database. The closest match to BR among the standard agents was tiazofurin, with a correlation coefficient of 0.76.

Dihydroorotate dehydrogenase inhibitors

Stowe et al. (86) confirmed COMPARE’s prediction that 2,2'-[3,3'-dimethoxy[1,1'-biphenyl]-(4,4'-diyl)diimino]bis(benzoic acid) (Redoxal) and 1-(p-bromophenyl)-2-methyl-1H-napth[2,3-d]imidazole-4,9-dione (BNID) were both dihydroorotate dehydrogenase (DHOD) inhibitors. Redoxal was identified by routine surveillance of screened output by using the standard agent database. Redoxal best correlated with the two DHOD inhibitors Brequinar and dichloroallyl lawsone. BNID, however, was identified by a retrospective COMPARE analysis against the entire synthetic compound database by using one of the known DHOD inhibitors as the seed.

References
 

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