Pharmacology/Pharmacognosy Lab
HDF maintains sufficient laboratory space to facilitate most in vitro and in vivo research requirements, especially if the nature of the research is relevant to either AD or epilepsy. Presently our pharmacology laboratory is configured for behavioral studies. Specifically, we currently employ both the Barnes maze, and an eight-arm radial maze in the study of compounds of interest in AD therapeutics. We use these tools in the study of learning and memory in transgenic models of AD, and the efficacy of our compound library in such models. Anticonvulsant testing is also done here. A separately ventilated, environment-controlled vivarium is available to house the small animals used in Alzheimer’s disease (AD) studies. A licensed veterinarian is on our consulting staff for guidance in matters related to animal care.
Yet other on-site facilities are structured to enable the pursuit of staff interests in natural product pharmacology. Strictly defined, pharmacognosy is the pharmacology of natural products and their derivatives. This scientific field typically encompasses activities to include natural product extractions, and the study of extract efficacy in an experimental model of a particular disease of interest. In the case of botanicals, a typical extraction might involve a series of solvents, with a range of polarities. The resolution and separation of the constituent chemicals virtually always requires one or more of the various forms of chromatography. Properly applied, such chromatographic efforts are typically successful.
However, such studies often fail, for a variety of reasons not the least of which being a very low concentration of active chemical in such extracts. Interfering actions and interactions also limit the usefulness of this approach. Thus, most successful studies of natural product therapeutics require chemical separation, purification and structure identification; and ultimately evaluation of the efficacy of each purified chemical is studied in an experimental model of a particular disease of interest.
For a variety of reasons, usually based on history (including folklore), botanical extracts may be obtained from leaves, roots, stems, or the entire plant. Once purity of the chromatographic fractions is assured (by crystallization, thin layer chromatography, melting point, etc.) structural identification of constituent molecules may involve a variety of spectroscopic tools, to include visible/UV, and FTIR spectroscopy; but mass spectrometry and NMR spectroscopy are virtually always required for definitive structure determination. Purified chemical fractions are next studied for efficacy in prokaryotic and eukaryotic systems, from bacterial and cell cultures to vertebrate mammals, depending on the therapeutic target.
A very logical first question is “What would cause a scientist to undertake the above complex and time-consuming effort in the absence of prior evidence that a particular natural product might have their desired efficacy?” History of use, often based in folklore, has already been noted above. However, this is typically is not sufficient reason to undertake such an effort. So, what does it take?
Usually, we will have some concept of the chemical structure, or the substructure which we are seeking. Usually, it is what we might call a pharmacophore, a fragment of a larger molecule thought responsible for its therapeutic efficacy. Many natural product database searches are begun with the known structure of a putative pharmacophore discovered in quantitative structure activity (QSAR) studies. Molecular familiarity, possibly guided by prior experience and intuition sometimes helps. Most often we do not seek a precise structure. A substructure search of a natural product database is often adequate for the discovery of a pharmacophore identified by prior QSAR studies.
So, what are we looking for in a natural products database search? In the case of a botanical, we are attempting to discover the identity, the genus and species, and any variations known to contain a molecule within which is contained a so-called pharmacophore. Thus, in a natural product database search we seek all related sub structures of said chemical; and any so-called “hits” are attributed to the particular genus and species that has previously been found to contain the entity of interest.
Out of an abundance of caution we also seek structures and substructures from a diversity of synthetic data bases. But many consider it unwise to neglect natural products, because the richest source of chemical variation is to be found in natural products. If we discover our pharmacophore of interest appended to a natural product of considerable structural diversity, we sometimes discover advantages in both pharmacokinetics and pharmacodynamics. This is sometimes seen in the development of antineoplastic drugs and antibiotics. Nonetheless, it is unusual that the parent compound is the chemical that finds its way into therapeutics. The reason for this is that molecular pruning is in most cases necessary in order to optimize both pharmacokinetics and pharmacodynamics. While we look toward natural products for new drugs, the chemical structure ultimately employed is most often a derivative of such structures.
Often times the most difficult task becomes the acquisition of the natural product from which the target chemical must be derived. We have met with considerable success in the identification of natural products that show efficacy as Aβ aggregation inhibitors; but frequently we find we must improve bioavailability and efficacy of such chemicals by modifications of their chemical structure.
Having discussed in some detail the scientific field of pharmacognosy, it should nonetheless be acknowledged that our greatest success in lead compound discovery has been that derived from de novo organic/medicinal chemistry efforts in the synthetic laboratory. This effort has been facilitated by our work in the computational chemistry lab. As we near completion of our in vitro work, we now prepare to study our lead compounds in small animal models of AD.