Predicting Polymorph Risk Before Formulation Locks in a Natural Product Candidate Through Crystal Landscape Reasoning and Solid-State Uncertainty Assessment

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Polymorphism creates a development problem before it becomes a formulation problem. A natural product candidate may appear to have an acceptable solid form while its accessible crystal landscape remains incompletely sampled, its energetic ordering uncertain, or its crystallization behavior sensitive to solvent, temperature, and molecular conformation. This article develops a proposed decision framework for assessing polymorph risk before formulation choices become difficult to reverse. The analysis separates four questions that are often conflated: which crystal structures are plausible, which are thermodynamically competitive under relevant conditions, which are kinetically accessible, and which alternative forms would materially affect development. Recent advances in crystal structure prediction, machine-learned intermolecular potentials, finite-temperature free-energy treatment, and orthogonal solid-state characterization strengthen early risk assessment, but none eliminates uncertainty in landscape completeness or experimental accessibility. The framework therefore treats polymorph risk as an evidence-integration problem rather than a request for a single predicted structure or energy ranking. Molecular flexibility, packing compensation, solvent-dependent stability, finite-temperature landscape reduction, and late or elusive polymorph discovery are interpreted as distinct uncertainty sources that should alter the intensity of experimental escalation. Natural-product examples further show why complex intermolecular recognition cannot be reduced to simple flexibility or hydrogen-bond counts. The proposed framework is nonempirical and requires prospective validation across chemically diverse candidates. It is intended to support earlier, explicitly qualified solid-state decisions, not to replace experimental screening, establish universal risk thresholds, or predict formulation performance with validated accuracy.