PDT's Validation
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.
Drug formulations
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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
In-vitro protein synthesis
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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
Synergistic drug combinations
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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.