Natural Science > Chemistry > DG CY2020 7787

Research Title PCOPEIA: Predictive Chromatography of Organic Plant Extracts with Intelligent Agents (Year 3 of 3)
Research Personnel Leader:

Research Duration Start:
1 November 2017
31 October 2020
Research Location TIP
Research Description The project will utilize a novel in silico tool widely known as machine learning, to modify/control the environmental and extraction parameters that would influence the reproducibility of the clinically significant profiles. The team will develop a Deep Learning Neural Network that would automatically determine the pattern of connections between input and output variables. This new method, which is coined as predictive chromatography, might enhance the efficiency of discovering drugs from extracts, by eliminating much of the time taken to analyze the sample in the lab.
Research Objectives 1) To develop a remote monitoring system for environmental conditions at the sambong plantation; 2) To design the data collection and processing protocols toward sambong extraction; 3) To collect input-output datasets related to sambong's HPLC fingerprints; 4) To train a neural network via iterative learning of the statistical, possibly nonlinear, relationships between input and output toward predicting chromatographic profiles; 5) To connect the study with bioassay results.
Research Beneficiary(ies) 1) Pascual Pharma Corp. for its continued leadership as an innovator in the natural products sector of the local pharmaceutical industry; 2) Botanical gardens, for the profiling of compounds that could be extracted from their plant collections for possible medical applications; 3) Drug discovery data banks and screening libraries, for contributing extracts data; 4) Pharmaceutical companies, for the verification of therapeutic claims; 5) Philippine Government, in support of its drug discovery project.
Research Accoplishments [CY 2017] Novel correlations between environmental conditions and HPLC profile of sambong; Optimization of extraction parameters to enhance the reproducibility of HPLC signatures with potential clinically metabolic significance; Remotemonitoring instrumentation system for the environment conditions that influence plant growth and development linked to a machine learning program; The name "PCOPEIA" as a trademark; Systematic extraction protocols; Data analytic software for predictive chromatography of plant extracts; Big data framework for assessing the electroactive profile of other plants with putative medicinal properties; Traning of chemistry students about extraction for natural products; Training in the operation of LC-MS device fro chromatographic analysis; A control center that receives and processes (for machine learning) the remote data from the internet-linked instrumentation system at the plantation.
Total Research Cost ₱112,417.95
Research Agencies Funding:


Research Budget Breakdown Year:
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Code DG CY2020 7787
KRA Code
Priority Thrust DOST

Sector Natural Sciences
Actual Sector Chemistry
Related sectors Plant Science
Entry revision: February 2021