ProtoLife Inc., San Francisco, California, United States of America.
PLoS One. 2010 Jan 1;5(1):e8546. doi: 10.1371/journal.pone.0008546.
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.
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.
我们研究了优化脂质体药物制剂的问题：这是一个复杂的化学体系，包含许多成分（例如，脂质库的元素），它们以非线性和协同的方式相互作用，无法从第一性原理预测。
方法/主要发现：我们实验中的优化标准是目标药物两性霉素 B 的包封百分比，通过分光光度法实验检测到。如此复杂体系的优化需要采用能够在潜在实验空间的极大体积中高效发现解决方案的策略。我们设计并实施了一种新的实验设计进化策略（Evo-DoE），通过在迭代循环中结合计算机和统计建模的强大功能与实验测量的响应，有效地探索高维空间。
我们展示了建模和实验的迭代循环如何快速产生具有显著更好实验响应的新发现，以及这种循环如何发现复杂化学体系的底层化学景观。
