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Finding a neuro-clinical indication for synthetic or herbal preparations
I remember the day in 1979 when I just had decided to move from the university campus into industry in order to get engaged in drug development at E. Merck in Darmstadt, Germany. Within a short period many colleagues dropped into the lab to see what the new pharmacologist would look like. At that particular day a chemist draw my attention to a small vessel in his hand containing a white powder and made a statement which I shall never forget: “You are the new pharmacologist? Well, I have synthesized a very exciting compound which I believe should act on the brain, and I think it is your job to tell me what it is good for in terms of disease.” What I did not know at that time was that it would take me about twenty years to develop and validate a methodology which in general can answer that kind of question.
Fig. 1 Electropharmacograms of the action of several drugs acting on diverse neurotransmitters. Pre-post drug comparison in terms of electric power in % of the baseline values for each of the 6 frequency ranges (delta - beta2).
Details of the methoology
There are numerous approaches in drug development to characterize a new chemical with respect to its action on the brain. The classical way consists in injecting the compound into a mouse at a dose of for example 300 mg/kg and to see what kind of behavior is observed. The next step might involve a so-called receptor screening where the interaction of the compound with known neurotransmitter receptors is tested. Then, the new chemical usually enters secondary drug screening models, which very often are “disease specific” like for example the forced swimming test according to Porsolt, which is thought to be able to detect compounds with a potential to treat depression. Thus, many animal experiments are needed to characterize the action of a new compound and to predict the potential use in human therapy. Was there a possibility to shortcut this huge effort?
After a thorough look into literature I came to the conclusion that electrical signals in form of the field potentials would contain exactly the kind of information I was looking for. The electro-chemical communication structure of the brain seemed to open the possibility to listen to the “language of the brain” - as I called it - by recording field potentials at different locations of the brain. Since more and more evidence came up in favor of the idea that electrical signaling within the brain governs not only motor actions but also guides cognition and emotion, decoding of these signals at a higher level of nervous activity promised a fascinating way of learning more on the mechanism how drugs interact with the brain. Based on this kind of reasoning I developed a methodology called “Tele-Stereo-EEG” in the freely moving rat hoping that I would find similar features later in humans by recording the electroencephalogram, better known as the ”EEG”. In other words, I saw a chance to measure parameters which I could carry over into clinical research and drug characterization in man.
The Electropharmacogram in rats
The changes of electrical activity (field potentials) at several locations of the brain recorded in the presence of a (potential) drug in comparison to its activity without the drug are called an “Electropharmacogram”. In my set up it is constructed from data recorded from frontal cortex, hippocampus, striatum and reticular formation and contains information on the following important questions during drug development:
• Does the drug reach the brain and produce a change of activity at all?
• Is its action dose dependent (i.e. is it linear with change of dosage)?
• Is its action time dependent (i.e. lag phase due to metabolism)?
• Is it possible to test for drug-drug interaction?
• Has the pattern of changes been seen before (comparison to data base)?
• Is there a similarity to the actions of compounds acting selectively on particular neurotransmitter receptors or ion channels?
• Is there a similarity to well known drugs with known clinical indication?
• Is there a difference between acute and repetitive action?
In order to be able to answer the last questions a large data base had to be constructed over the years. This database currently contains more than 200 chemicals and drugs with at least three dosages (about 30 000 hours of recording) and covers at least the 8 textbook indications (anaesthetics, analgesics, antidepressives/neuroprotectives, antidementives, antiepileptics, hallucinogenics, neuroleptics, stimulants). In addition compounds acting at the acetylcholine-, norepinephrine-, serotonin-, dopamine-, glutamine- and GABA-dependent neurotransmission have been characterized. Examples will be given below.
One of the most important preconditions for recording electrical activity (field potentials) from living, freely moving animals was the development of a wireless transmitter by French space agency “SFENA”. It was used to learn more on the activity of the brain under space conditions. I got first access to this technology in 1984. Basically, four semi-microelectrodes are fixed to a plate which carries a small plug . Rats are day-night converted to achieve stable electrophysiological recordings. Within a small surgical procedure this implant is fixed to the skull of a rat by means of dental cement attached to three steel screws drilled into the bone. The animals are given 2 weeks for recovery. During an experiment thereafter, the wireless transmitter is plugged into the implant. The experiment usually lasts nearly 6 hours (after a baseline recording of 45 minutes, the drug is administered and its action is followed for the next 5 hours). The electrical activity is monitored continuously during this time with a time resolution of 4 seconds. These periods are averaged to give periods of 15 minutes or longer in order to document drug induced changes of electrical activity.
Neuronal electrical activity depends on the active set of ion conductances at a given time. These are switched by the activity of several neurotransmitters or by direct interaction of chemicals with the ion channel (big protein) itself. Simultaneous activity of a larger number of neurons is recorded as a so-called field potential comparable to the electrical activity (potential changes) at a single EEG electrode at the human scalp. An example of such a field potential is given in Fig. 3. In order to quantify this electrical activity a mathematical procedure is used named after the French mathematician “Fourier”. This transformation is rather complicated and takes so much calculation effort, that Hans Berger, the German discoverer of the human EEG, needed about 3 weeks in order to calculate the result for a very short time period of the EEG (Berger and Dietsch, 1932). Today, with the aid of computers this transformation (also called Fast Fourier Transformation or FFT) is no problem. Essentially, periods of 4 seconds duration are analyzed consecutively with respect to the question: which number and amplitudes of sine waves are needed to reconstruct the present signal? The result of this frequency analysis is depicted as a so-called power spectrum (documented in Fig. 1), where the amplitudes of the original signal appear on the ordinate as electrical power (Microvolt2/Ohm) and the frequency (Hertz) on the abscissa. In order to make comparison between such power spectra easier, the spectra are arbitrarily chopped into several segments with respect to frequencies (in our case 6). These are designated (historically) as delta (lowest frequency), theta, alpha1, alpha2, beta1 and beta2 waves (highest frequency). The spectral power within such a segment is averaged and given as one number or depicted as one column within a graph (integral of spectral power).
Fig. 2 From field potential (upper line) to frequency spectrum and integrated bar graph (base for electropharmacogram).
This value confines the parameter serving for comparison of field potentials or EEG`s in man. Changes of this parameter with respect to segmental power (i.e. delta – beta2) during drug action in various brain areas or electrode locations are the fundament of the Electropharmacogram. Examples of electropharmacograms of several synthetic drugs ar given in Fig. 2.
Fig. 3 Pre-post comparisons of drug action on frequency changes (% of baseline values) for each of the frequency ranges from delta to beta2.
Interpretation of Electropharmacograms
Once the Electropharmacogram has been constructed by the software, dose and time dependence of changes are immediately visible. The next steps aim at the comparison of the present result with earlier experiments. A first base for comparison is the relationship of frequency changes among each other. A program was designed which compared for example the changes in delta activity with those of the theta, alpha and beta changes for each of the electrode positions. All other data within the data base are screened for a similar relationship among the frequency segments. Finally, a “hitlist” is created which provides a kind of a “similarity index” and gives the statistical probability by which one could differentiate all other compounds from the present result. Drugs with a similar electropharmacogram are then depicted together within a graph in order to see the common denominator but also differences with respect to brain area and frequency changes.
Last not least, the data of the current experiment are fed into the mathematical procedure of discriminant analysis. This procedure uses 24 variables (6 frequency segments x four brain areas) to position the result within a polydimensional space. Its projection into the three dimensional (or sixdimensional) space allows to position the Electropharmacogram within a set of drugs with known clinical indication. Examples are given in Fig. 5a-c. From the cmparison of the plant derived preparation with a matrix of synthetic drugs with known clinical indications it becomes obvious that plant derived preparation are as efficient as synthetic drugs. Furthermore, this analyis proofs that the action of plant derived preparations or ingredients can be classified with respect to their main action into categories of clinical indications.
Since several neurotransmitters like acetylcholine, norepinephrine, serotonin, dopamine, glutamate and GABA are able to switch and modulate those ion channels responsible for the pattern of neuronal firing, any interference of potential drugs with synthesis, transport, release or binding of any of these neurotransmitters results in changes of electrical activity. Thus, the Electropharmacogram must contain information on neurotransmitter activity. In order to find out more about this relationship, compounds with known activity on selected neurotransmitters were administered hoping to see specific changes. Giving several drugs - all interfering with one of the neurotransmitter action – it became obvious that for example interference with cholinergic transmission resulted predominantly in changes of delta frequencies, whereas interference with the dopaminergic system could be attributed to spectral alpha2 frequency changes. Even if much work has to be done in this field there is some evidence that the serotonergic system in some way relates to alpha1 frequencies. But one has to await specific interactions with subtypes of neurotransmitter receptors before one can rely more on this interpretation. Further segmentation of the power spectrum might unravel more details of the Electropharmacogram with respect to neurotransmission and the interference of drugs with it.
Fig. 4 Position of plant-derived preparations or ingredients in relation to synthetic drugs with known clinical indication by means of discriminant analysis. For example Moc (moclobemide) appears near imipramine (imi) and paroxetine (par). Results from the first three discriminant functions are depicted on the x, y, and z axis. Results from the fourth to the sixth function are depicted as colour mixture (RGB-mode). Plant derived preparations are written in red colour. Upper panel shows Kavain and Hesperidin near analgesic drugs. Middle panel shows Quercetin, Rutin, Theanine and Avena sativa near antidepressant drugs. Lower panel shows Yangonine, Guarana and decaffeinated green tea near stimulants.
1. Dimpfel W, Decker H (1984) Classification of Drugs by Stereotactic Recording of Focal Brain Activity in the Rat (Stereo-EEG). Neuropsychobiology 12, 188 – 185.
2. Dimpfel W, Decker H (1985) Classification of Sulpiride, Clozapine and Haloperidol by Toposelective Recording from Different Brain Structures in the Immobilized Rat (Stereo-EEG). Neuropsychobiology 14, 157 – 164.
3. Dimpfel W (1985) Drug-induced field potential changes in dopaminergic target areas after electrical stimulation of the rat mesencephalon. Neuropsychobiology 14, 42 – 52.
4. Dimpfel W, Spüler M, Nickel B (1986) Radioelectroencephalography (Tele-Stereo-EGG) in the rat as a pharmacological model to differentiate the cental action of flupirtine from that of opiates, diazepam and phenobarbital. Neuropsychobiology 16, 163 - 168.
5. Dimpfel W, Spüler M, Nickel B, Tibes U (1986) "Fingerprints" of Central Stimulatory Drug Effects by Means of Quantitative Radioencephalography in the Rat (Tele-Stereo-EEG). Neuropsychobiology 15, 101 - 108.
6. Dimpfel W, Spüler M, Koch R, Schatton E (1987) Radioelectroencephalographic comparison of memantine with receptor-specific drugs acting on dopaminergic transmission in freely moving rats. Neuropsychobiology 18, 212 - 218.
7. Dimpfel W, Spüler M, Borbe H.O (1988) Monitoring of the effects of antidepressant drugs in the freely moving rat by radioelectroencephalograhy (Tele-Stereo-EEG). Neuropsychobiology 19, 116 – 120.
8. Dimpfel W, Spüler M, Nickel B (1988) Dose and time dependent action of morphine, tramadol and flupirtine as studied by radioelectroencephalography in the freely behaving rat. Neuropsychobiology 20, 164 – 168.
9. Dimpfel W, Spüler M, Menge H.G (1989) Effects of the antiparkinson drug budipine on EEG activity in freely moving rats. Arzneimittel-Forschung/Drug Research 39 (1) 5, 560 - 563.
10. Dimpfel W, Spüler M, Nichols D.E (1989) Hallucinogenic and stimulatory amphetamine derivates: Finger-printing DOM, DOI, DOB, MDMA and MBDB by spectral analysis of brain field potentials in the freely moving rat (Tele-Stereo-EEG). Psychopharmacology 98, 297 – 303.
11. Dimpfel W, Schindler U, Spüler M (1989) Radioelectroencephalographic Characterization of Tenilsetam (CAS 997), a Putative Nootropic Compound, in the Freely Moving Rat (Tele-Stereo-EEG). Drug Development Research 18, 217 – 227.
12. Dimpfel W, Spüler M, Borbe D (1990) Influence of Repeated Vitamin B Administration on the Frequency Pattern Analysed from Rat Brain Electrical Activity (Tele-Stereo-EEG). Klinische Wochenschrift 68, 136 – 141.
13. Dimpfel W, Spüler M (1990) Dizocilpine (MK 801), ketamine and phencylcidine: Low doses affect brain field potentials in the feely moving rat in the same way as activaton of dopaminergic transmission. Psychopharmacology 101, 317 – 323.
14. Dimpfel W, Spüler M, Wessel K (1992) Different neuroleptics show common dose and time dependent effects in quantitative field potential analysis in freely moving rats. Psychopharmacology 107, 195 – 202.
15. Dimpfel W, Wedekind W, Spüler M (1992) Field potential analysis in the feely moving rat during actions of cyclandelate or flunarizine. Pharmacological Research 25, 287 – 297.
16. Dimpfel W, Schombert L (1997) Central Action of Hyperici Herba Cum Flore Extractum Siccum In Freely Moving Rats. Eur. J. Med. Res. 2:491- 496.
17. Dimpfel W, Schober F, Mannel M (1998) Effects of a Methanolic Extract and a Hyperforin-Enriched CO2 Extract of St. John`s Wort (Hypericum perforatum) on Intracerebral Field Potentials in the Freely Moving Rat (Tele-Stereo-EEG). Pharmacopsychiat. 31 (Suppl.): 30-35.
18. Dimpfel W, Schober F (2001) Norepinephrine, EEG theta waves and Sedation in the rat. Brain Pharmacology, 1, 89-97.
19. Dimpfel W, Vonderheid-Guth B, Wedekind W (2001) Das quantitative EEG als elektrischer "Fingerprint" von Phytopharmaka bei Ratte und Mensch. Zeitschrift für Phytotherapie 22, 22-27.
20. Dimpfel W (2003) Preclinical Data Base of Pharmaco-specific Rat EEG Fingerprints (Tele-Stereo-EEG). Eur. J. Med. Res 8: 199-207.
21. Dimpfel W (2005) Pharmacological modulation of cholinergic brain activity and its reflection in special EEG frequency ranges from various brain areas in the freely moving rat (Tele-Stereo-EEG). European Neuropsychopharmacology 15:673-882.
22. Dimpfel W (2006) Reflection of dopaminergic activity within the alpha 2 frequencies of intracerebral field potentials in the freely moving rat (Tele-Stereo-EEG). (5th Forum of European Neuroscience - Vienna - Poster)
23. Dimpfel W, Brattström A, Koetter U (2006) Central action of a fixed valerian-hops extract combination (Ze 91019) in freely moving rats. Eur. J. Med. Res 11 496-500.
24. Dimpfel W, Kler A, Kriesl E, Lehnfeld R und Keplinger-Dimpfel I.K (2007) Theogallin and L-theanine as active ingredients in decaffeinated green tea extract: II. Characterization in the freely moving rat by means of quantitative field potential analysis. Journal of Pharmacy and Pharmacology JJP 59:10 1397-1403.
25. Dimpfel W (2007) Transmitter specific drug profiling using electropharma-cograms from freely moving rats: drug induced modulation of dopamine, serotonin and GABA. (20th ECNP Congress 2007 Vienna - Poster)
26. Dimpfel W (2007) Atypical Antipsychozic Drugs – Characterization by a late decreace of striatal alpha1 spectral power in the Electropharmacogram of freely moving rats. (20th ECNP Congress 2007 Vienna - Poster)
27. Dimpfel W (2007) Characterization of atypical antipsychotic drugs by a late decrease of striatal alpha1 spectral power in the electropharmacogram of freely moving rats. British Journal of Pharmacology 152, 538 – 548.
28. Dimpfel W (2007) Electropharmacograms of rasagiline, aminoindan and selegiline in the freely moving rat. (17th Int. Congress on Parkinson´s disease and related disorders- Amsterdam - Poster)
29. Dimpfel W (2007) Electropharmacograms of Pramipexol in the freely moving rat. (17th Int. Congress on Parkinson´s disease and related disorders - Amsterdam- Poster)
30. Dimpfel W (2008) Pharmacological modulation of dopaminergic brain activity and its reflection in spectral frequencies of the rat electropharmacogram. (Journal of Neuropsychobiology submitted 2008)
31. Dimpfel W (2008) Rat Electropharmacograms of Rutin and Quercetin in Comparison to those of Moclobemide and clinically used Reference Drugs suggest antidepressive and/or neuroprotective action. (Journal of Pharmacy and Pharmacology JJP submitted 2008)