From Biodiversity to Mechanism: Activity-Directed Discovery in Marine natural products, chemical biology and drug discovery

Biodiversity to Mechanism showcases one of the major challenges in marine natural products and activity-directed chemical biology: the huge chemical diversity among the species of the biosphere massively outnumbered the new and distinct mechanism of action that may be therapeutically relevant. The more successful development strategy is based upon a measurable mechanism and not just an impressive assay result. The transition makes chemistry, biology, exposure, resistance, safety, and production a single argument of translation.

Underlying science is the idea of activity-directed discovery which involves the extraction and fractionation of biological signal, their structural assignment, target identification, and functional validation. The mechanistic claim must be expressed in terms of each individual component, otherwise no isolated measurement could carry the entire mechanistic claim. By linking evidence, the probability of mistaking potency, binding, formulation performance or model fit for therapeutic understanding is minimized.

In particular, activity directed workflows are useful for marine samples as dozens or hundreds of metabolites can exist in a crude extract and their concentration may vary as the cultivation and collection conditions change. Molecular networking and high resolution mass spectrometry are used to associate chromatographic features with bioactivity and purification and NMR are used to determine structure. Recombining the purified constituents also allows to determine if a phenotype was determined by one molecule or by interactions among the molecules in the mixture.

Given that there can be a change in the performance of the design variables such as taxonomy, habitat, cultivation state, extraction polarity, chromatographic fraction, abundance of metabolites, assay context, and formulation state, all occurring simultaneously, these variables are required to be treated jointly. The benefit of making the best of one parameter at a time can sometimes come with an unrecognized cost for another parameter, particularly when potency is matched against stability, selectivity, delivery, and/or manufacturability. Multiparameter decision rules are thus a more realistic criterion for ranking candidates than a single number.

Early measurement of failure routes is also a key factor in establishing mechanistic confidence. Signal dilution, synergistic mixtures, unstable metabolites, nonspecific membrane effects, target redundancy and adaptive pathway compensation are all important escape mechanisms. Deliberate stress testing transforms these routes into design information which can be used for the next chemistry cycle, formulation, dosing or more model cycles.

Exposure is the link between the nominal dose and the biological effect. Parameters include free concentration, cellular uptake, metabolite stability, protein binding, intracellular distribution, pharmacologically relevant duration etc. Strong intrinsic activity and weak exposure at the causal site may present a candidate with a great performance in simplified assays, but be unsuitable for translation.

Realistic concentrations must be used for interpretation. High doses can induce secondary stress response, broad off-target effects or assay artifacts that can simulate a desired mechanism. Concentration-response analysis, coupled with exposure and occupancy, can be used to pinpoint a range where the proposed causal process is thought to have an action.

Further, biological context influences interpretation at the level of microbial communities associated with marine microorganisms and invertebrates. Target dependence and effective exposure can vary between individuals due to differences in genotype, cell state, tissue architecture, immune tone, metabolic environment and previous treatment. Representative model panels are thus more convincing than multiple testing in one system.

Resistance and adaptation should be measured in the future not the past. Mechanisms that persist under repeated stresses such as serial passage, long-term selection, genetic perturbation, repeated dosing or environmental stress are revealed. It's better to have a less effective candidate with a higher probability of escape than it is to have a weak screening candidate.

The combination of safety assessment with mechanism provides a more informative assessment. Some issues that are important are: counter-screens in primary cells; integrity tests of membranes; mitochondrial stress; wide reactivity panels; and target-family selectivity. A comparison of desired from unwanted biological responses informs on the distinction between mechanism-related pharmacology and broad-based cellular injury and formulation-related toxicity.

The sustainability of the sources, the feasibility of aquaculture or fermentation, the reproducible biomass, the scalability of the purification, the semi-synthetic access and the reference standards are factors that affect the product that is delivered to the biological systems and therefore cannot be a downstream logistics exercise. Small changes in the product can result in product changes that impact product composition, physical state, level of impurities, stability, or dose reproducibility even if the product name does not change. Strengthening the development through direct connections of critical process attributes with the mechanistic performance.

The computational tools provide leverage by integrating feature-based molecular networking, spectral annotation, activity correlation, multivariate profiling and causal prioritization of fractions. There is most usefulness in prediction when there is ambiguity and applicability limits still present, particularly where there are new chemical structures, unusual biological states or where there is little data. Models that reveal uncertainty allow for improved experiment selection than models that report confident rankings without revealing areas of uncertainty.

Prospective validation involves other experimental factors: recombined fraction experiments, purified compound confirmation, synthetic standards, target perturbation, rescue studies, and independent collection batches. Orthogonal tests are important because if repeated, tests from the same assay family can give the same hidden bias which doesn't strengthen the causality. When two or more independent methods agree there is greater confidence in the link from design to biological effect; when they disagree, then the assumptions must be changed.

Data integration should maintain the traceability of the data, from the raw experiment to the decision made for the development. Chemical structures, assay conditions, assay metadata, exposure data, omics data and resistance data and safety signals must be identified and versioned. This discipline will keep the interpretation from straying from the actual circumstances that created a result later on.

Decision metrics also must incorporate translation and not laboratory show. Scorecards are useful to have that encompass: Exposure-normalized activity, mechanistic confidence, resistance frequency, safety margin, stability, and manufacturing robustness, along with reproducibility. This will encourage balanced candidates and allow for better trade-offs to be seen before major development investments are made.

In this context, negative result is a special value. The edge of a hypothesis is determined by: Failed synthesis, missing target engagement, unstable exposure, absence of rescue, emergence of resistance, poor batch reproducibility, loss of activity in realistic models. Documenting failures helps to enhance the design of future experiments, and avoids reinvestments in appealing, but unsupported explanations.

The reproducibility between laboratories and batches is another crucial filter. When it appears the results are significant, a mechanism relying on a single operator, single preparation, single cell line, or single modeling option is inherently fragile. When conditions are repeated independently, and with exposure matched, it is then a matter of durable biology or local experimental success.

There is also a practical requirement for a translation in clinical and/or deployment conditions. The value of a technically advanced product may depend on the route, administrative requirements, diagnostic needs, storage requirements, monitoring requirements, costs and timing of treatment. Mechanistic excellence has impact when the product design is appropriate in the context in which biological benefit needs to happen.

Temporal design also contributes to the complexity since the responses of biological systems vary when short pulses, exposure, washout, and repeated doses are applied. Time resolved measurements can help to distinguish primary engagement from downstream adaptation, and can show if there is any early activity that lasts after the initiating signal has subsided. Sampling protocols should then be tailored to the anticipated dynamics of natural products from marine organisms and activity-driven, chemical biology-based methods – not a single endpoint.

An integrated development record (IDR) helps to facilitate better communication among medicinal chemistry, formulation science, pharmacology, computation, manufacturing and clinical planning. The record may not present a single definitive story, but instead, it can differentiate clear connections, reasonable guesses, unknowns, and possible next experiments with the greatest amount of information. With that transparency, iteration becomes quicker and there's less pressure to stick to initial assumptions in the face of conflicting data.

Novelty is held in check by reference to credible reference conditions. Whether a new approach is an improvement in prediction, mechanism, durability or manufacturability is determined by established compounds, inactive compounds, standard formulation, baseline models or accepted analytical methods. Head-to-head comparison also allows a comparison of gains that are lost after normalizing the data for exposure, assay sensitivity, batch quality, or experimental complexity.

When stopping rules (which are specified beforehand) are used to end the process of synthesis and testing if the hypothesis is not strong enough, decision quality is further improved. Resources are targeted specifically to mechanisms and show clear progression criteria, which allow mechanisms to be repeated with increasing biological and operational complexity.

Only when all enrichment steps leave a repeatable causality, is there ‘mechanism’ from the activity-directed discovery of marine biodiversity. Whether a candidate wins one assay is not the important thing; what is important is that the proposed mechanism holds together through the different stages of exposure, biological variation, resistance, safety and production. That standard puts in place a more "disciplined" way to go from discovery signal to a therapeutic concept that will be able to stand the complexity of the real world.