Diseases such as Chagas disease, leishmaniasis and human African trypanosomiasis affect populations with little commercial purchasing power, and the drugs available for them are old, toxic or awkward to administer. The road map published by the World Health Organization (2021) sets targets for control that the current pharmacopoeia is not adequate to meet. Repurposing an approved medicine shortens development because human pharmacokinetics and safety are already documented, an argument reviewed with its practical obstacles by Pushpakom et al. (2019).

Antifungal agents are a natural set to examine. The azoles act on sterol 14-demethylase, a cytochrome P450 enzyme that catalyses an essential step in sterol biosynthesis and that is present across fungi, plants, animals and trypanosomatid protozoa (Lepesheva and Waterman, 2007). The mechanisms of the main antifungal classes are well characterised (Odds et al., 2003), and interest in azole inhibition of the trypanosomatid enzyme has a long history (Buckner and Urbina, 2012). The set of approved antifungals is small enough that every member can be docked, inspected by eye and discussed.

What is missing is not computing power but discipline. Docking a few dozen known drugs into a homology model and reporting the best scores is quick, publishable and of little consequence. This article proposes a workflow that makes such a screen worth doing: it fixes the decisions in advance, requires a validation step before any prediction is believed, and ends in a committed list for experimental testing. The workflow is a proposal. It has not been applied to a target here, and no docking results are reported.

Two bodies of work inform the design. The first concerns the reliability of docking. Comparative assessments have repeatedly found that docking programs reproduce crystallographic poses reasonably often while ranking compounds by affinity poorly (Warren et al., 2006), and Chen (2015) catalogued the ways in which docking results are routinely over-interpreted. The practical guidance assembled by Bender et al. (2021) treats retrospective validation against known ligands and decoys as a precondition rather than an optional extra, and large prospective campaigns such as the one reported by Lyu et al. (2019) succeed partly because they test many predictions experimentally.

The second concerns repurposing itself. A compound optimised against one organism carries the properties it was optimised for, including a distribution profile suited to its original indication. Potency against a purified enzyme is therefore a weak predictor of clinical usefulness, and the history of azoles in Chagas disease illustrates the point: encouraging activity against the parasite enzyme did not carry through, and a randomised trial of posaconazole in chronic infection reported inferior parasitological efficacy compared with benznidazole (Molina et al., 2014). A workflow that ends at a docking score ignores exactly the step where the difficulty lies, a concern raised in the wider discussion of lead discovery for tropical diseases by Nwaka and Hudson (2006).

The workflow has five stages, shown in Figure 1. The loop in the figure is deliberate: a stage that fails its stop condition returns to the preceding one rather than passing forward a weak result. Table 1 states the condition for each stage and the record it leaves.

Table 1 collects the stop conditions. They are deliberately strict, because the cost of proceeding with an inadequate receptor or an unvalidated protocol is borne later, by the collaborator who spends laboratory time on a prediction that was never sound.

The computational demands are slight. A set of a few dozen approved drugs docked into one site runs on a laptop in hours, and the open tools required are freely available. The costs lie elsewhere: in the time of a chemist who can judge a pose, in access to a laboratory able to run the parasite assay, and in the discipline of writing the rationale before the results. Groups in endemic countries often have the second of those and lack the first, which argues for collaboration rather than for more compute.

Reporting deserves the same care. A screen reported with its validation step, its full score table and its committed test list is useful to the next group even when nothing active is found. A screen reported only as a ranked list of hopeful compounds adds to a literature that is already crowded with them.

Three limitations are intrinsic. Docking scores correlate weakly with measured affinity (Warren et al., 2006), so the workflow should be understood as a way of ordering a small set for testing and not as a prediction of potency. Predicted receptor structures may be accurate in the fold and wrong in the side chain conformations that determine the fit, which matters most for a buried site. And activity against an isolated enzyme does not establish activity in the parasite, let alone in a patient, as the azole experience in Chagas disease shows (Molina et al., 2014).

What remains to be shown is whether the workflow improves on current practice. Two tests suggest themselves. One is retrospective: applying it to targets for which the outcome of an antifungal repurposing attempt is already published, and asking whether the stop conditions would have prevented the attempts that failed for computational reasons. The other is prospective and collaborative: several groups applying the same workflow to different targets, reporting the committed lists and the assay results whatever they are. Neither has been done here, and until one of them is, the workflow should be read as a proposal.

Screening approved antifungals against a conserved target in a neglected pathogen is cheap, and cheapness is what makes a disciplined procedure worth having. The five-stage workflow proposed here places the target rationale and the protocol validation before any prediction, keeps two scoring functions in play, insists on human inspection of poses, and fixes the experimental test list in advance. It is assembled from published docking practice and repurposing guidance rather than from new results, and its value is an empirical question. Groups working on these diseases, who carry the cost of unproductive predictions, are the ones best placed to settle it.

To be declared by the author(s) before publication.

To be declared by the author(s) before publication.