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Automated Discovery of Novel Drug Formulations Using Predictive Iterated High Throughput Experimentation

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  • Filippo Caschera
  • Gianluca Gazzola
  • Mark A Bedau
  • Carolina Bosch Moreno
  • Andrew Buchanan
  • James Cawse
  • Norman Packard
  • Martin M Hanczyc

Abstract

Background: We consider the problem of optimizing a liposomal drug formulation: a complex chemical system with many components (e.g., elements of a lipid library) that interact nonlinearly and synergistically in ways that cannot be predicted from first principles. Methodology/Principal Findings: The optimization criterion in our experiments was the percent encapsulation of a target drug, Amphotericin B, detected experimentally via spectrophotometric assay. Optimization of such a complex system requires strategies that efficiently discover solutions in extremely large volumes of potential experimental space. We have designed and implemented a new strategy of evolutionary design of experiments (Evo-DoE), that efficiently explores high-dimensional spaces by coupling the power of computer and statistical modeling with experimentally measured responses in an iterative loop. Conclusions: We demonstrate how iterative looping of modeling and experimentation can quickly produce new discoveries with significantly better experimental response, and how such looping can discover the chemical landscape underlying complex chemical systems.

Suggested Citation

  • Filippo Caschera & Gianluca Gazzola & Mark A Bedau & Carolina Bosch Moreno & Andrew Buchanan & James Cawse & Norman Packard & Martin M Hanczyc, 2010. "Automated Discovery of Novel Drug Formulations Using Predictive Iterated High Throughput Experimentation," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-8, January.
    • Handle: RePEc:plo:pone00:0008546
    DOI: 10.1371/journal.pone.0008546
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    References listed on IDEAS

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    1. Ross D. King & Kenneth E. Whelan & Ffion M. Jones & Philip G. K. Reiser & Christopher H. Bryant & Stephen H. Muggleton & Douglas B. Kell & Stephen G. Oliver, 2004. "Functional genomic hypothesis generation and experimentation by a robot scientist," Nature, Nature, vol. 427(6971), pages 247-252, January.
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