Predictive Design Technology (PDT) gets results. Three examples of successful application of PDT to experimental campaigns in the laboratory are described below. Many other kinds of complex discovery and optimization problems can be solved by PDT.
AmBisome™ is the leading formulation of Amphotericin B, an anti-fungal drug. PDT was applied to find novel formulations of the drug, optimized for cargo capacity.
A variety of amphiphilic molecules, salts, buffers, and other components made up an experimental space of over 80,000 experiments.
By exploring less than 0.6% of the possible experiments, PDT found
hundreds of novel formulations, many of which were very stable
and substantially increased cargo capacity.1
PDT has been used to optimize the amount of functional protein synthesized with a commercial in vitro synthesis kit.2 The experimental parameters included many possible DNA sequences, amino acid proportions, salts, buffers, and process variables.
This was a huge experimental space, and systematically exploring every experiment (over 1.5 million) would have taken an enormous amount of time and would have been extremely expensive.
PDT increased protein yield by 350% after exploring less than 0.015% of the experimental space.3
A pharmaceutical company, CombinatoRx, exhaustively screened many combinations of drugs, looking for synergistic therapeutic effects.4 This took months of time and hundreds of thousands of individual experiments. When applied to the data from this experiment, PDT found the optimal combination in the experimental space with less than 10% of the experimental effort.
1 Caschera, F. et al. (2010). Automated discovery of novel drug formulations using predictive iterated high-throughput experimentation. PLoS One, 5(1):e8546.
2 Invitrogen Expressway; Cell-Free E. coli Expression System.
3 Caschera, F. et al. (2011). Coping with complexity: machine learning optimization of cell-free protein synthesis. Biotechnology and Bioengineering, 108(9): 2218-28.
4 Lehar, J. et al. (2007). Chemical combination effects predict connectivity in biological systems. Molecular Systems Biology, 3(80): 1-14.