I have wondered about this same question. When I attend local group meetings, we all clearly suffer from something similar, but symptoms and progression vary a lot. i fully expect that we will eventually tag several different sub diseases (just like hepatitis A,B &C). Maybe GDNF is the perfect treatment for one type of Parkinson's and ineffective for others. How do we design that trial? Can the blood tests that at being tested in labs help to tease out these sub-types.

One thing that could be done, even prior to having a real understanding of the number and nature of the various subtypes of the disease that may exist, is to use "adaptive trial design," in which the investigator keeps tabs on the clinical trial data as it comes in during the various phases. Adaptive trial design would allow, for example, the investigator to divide the clinical trial population for a particular therapy in two patient populations, such as responders and nonresponders. After the division, the clinical trials could continue with the responders. If, further clinical trial data confirms that the responders continue to show statistically significant improvement, the sponsoring company could then make a decision as to whether this subgroup was sufficiently large to continue to develop the product with the expectation of ending up with a profitable therapy.

Adaptive clinical trial design is being actively discussed by researchers and the scientific community. It is, perhaps, one way out of the box that current clinical trial design protocol for Parkinson's has created.

Thanks, Greg!

"When drugs fail to work in humans, it's often because the agents "don't engage the drug target, or the target in animals does not correspond to the pathophysiology of the disease in humans," says Malcolm MacCoss, vice president for basic chemistry and drug discovery sciences at Merck & Co., Rahway, N.J. "So looking at the horizon, the next round of things we have to be very aware of is validating the targets we work on and reevaluating the value of the animal models of some diseases. If you have to wait for a full-blown clinical trial before finding out that a compound is going to work on a disease state, you're going to waste an awful lot of time, money, and manpower." In the future, MacCoss says, "the folks who will likely be most successful at drug discovery will be those who are able to validate drug targets fastest."

"Once target validation has been completed, the full drug discovery capability of a pharmaceutical company can be focused with more confidence on identifying and developing drugs specific for the confirmed target," says Oliver C. Steinbach, head of functional genomics at Altana Pharma, Konstanz, Germany.

"A target is not truly validated until a drug is proven effective in human trials," Steinbach notes. Nevertheless, efforts are generally made to validate targets way before the clinical trial stage.

A principal way this has been accomplished in recent years has been with gene knockdowns or knockouts, in which the expression of a gene for a target in a living animal is cut back drastically or eliminated entirely to see what effects that has.

Knockouts generally work well for target validation. "A retrospective study using mouse knockouts to assess the validity of targets of the 100 best-selling drugs demonstrated that in the vast majority of cases there is a direct correlation between the knockout phenotype [physical characteristics of the knockout mouse] and the proven clinical efficacy of drugs that modulate the specific target" knocked out in that mouse, Brown says.

In several respects, however, use of knockout animal models to validate drug targets is problematic. "Mouse gene-knockout technology provides a powerful means of elucidating gene function in vivo, but it is a tedious and time-consuming approach," Steinbach says. In addition, "published knockouts exist for only about 10% of mouse genes; many [knockouts] are limited in utility because they have not been made or phenotyped in standardized ways, and many are not freely available for use by researchers."

Efforts are being made to make knockout technology easier to carry out and more accessible. For example, an NIH-led initiative called KOMP, the Knockout Mouse Project, aims to produce a publicly available library of knockout mice. Each mouse will contain a knockout in one gene, and the library will range across the entire mouse genome.

Scientists have also been on the lookout for alternatives, or at least complements, to conventional knockouts. The most promising one so far has been RNA interference (RNAi), a technique in which a double-stranded RNA fragment called a short interfering RNA (siRNA) is used to degrade a messenger RNA and thus silence the gene that the mRNA is helping to translate into protein. RNAi can generally silence a gene in less time and at lower cost than is possible with conventional knockout animal models.

RNAi is thus of growing importance for target validation, and research on the technique is very active. For example, professor of biochemistry and molecular biology Miles Wilkinson of the University of Texas M. D. Anderson Cancer Center, Houston, and coworkers recently demonstrated a way to use RNAi to silence selected genes in specific cell types or tissues in a mouse to determine gene function or to explore therapeutic applications.

RNAi is "a valid approach for sequence-specific suppression of gene expression and hence inhibition of the corresponding gene function in a cellular context," Steinbach says, but it's not free from disadvantages. As with other technologies that interfere with biological systems, "there is the risk of artifacts, false positives, and false negatives," he says, but "with reasonable efforts, these can be managed and mitigated to a certain extent."

Moitreyee Chatterjee-Kishore, principal research scientist at Wyeth Research, Cambridge, Mass., agrees that RNAi "is a cost-effective addition to the target validation toolbox. These reagents can be easily and effectively used in a large number of cell types. The list of cells that are transfectable [capable of being treated] with chemically synthesized RNAi reagents grows every day with the availability of newer, less toxic, and more efficacious delivery options."

Target validation with RNAi-based knockouts takes much less time and eventually will be less costly than with traditional knockouts, Chatterjee-Kishore says. However, RNAi-induced downregulation of gene and protein expression may have significantly different effects from those induced by conventional knockout technology. Hence, "RNAi is not the be-all and end-all of target validation technologies, and for optimal efficacy it must be used in conjunction with other platforms," she says.

Another approach that is up and coming for analyzing the way drugs interact with biological targets is systems biology. Systems biology is the study of relationships and interactions among various parts of biological systems or pathways. It can thus be used to better understand how to intervene medicinally in biological pathways.

"At the moment we have a huge amount of very detailed and interesting information, but what we don't know is how it all knits together in dictating how a cell or organ or organism responds to a particular stimulus, challenge, or drug therapy," Henney explains. "That's the aspiration and goal of systems biology. It's being able to use computational methods to model, predict, and simulate complex interactions" in a way that would be impractical with a single-target approach.

Just understanding how a single molecule operates "is not sufficient to be able to understand how a particular medicine is going to respond in an intact organism," Henney says. Given "the way we've taken a reductionist approach to investigating targets in isolation, it's not surprising that when you put them back into the intact organism you get some surprises. That's what systems biology is trying to address. It's trying to understand the complexity of interactions underpinning a particular biological effect." It could also be useful for assessing drug toxicology, he predicts.

Systems biology "is complex, demanding, and by no means a cakewalk," Henney says. "But the potential there is for it to be applied to answer some critical questions with some measure of success. It's a logical next step.""

http://pubs3.acs.org/cen/pharma/8425...ml?print?print

Chemical and Engineering News:

June 19, 2006
Volume 84, Number 25
pp. 56-78

Improving Efficiency
To eliminate R&D bottlenecks, drug companies are evaluating all phases of discovery and development and are using novel approaches to speed them up

Multiregional Gene Expression Profiling Identifies MRPS6 as a Possible Candidate Gene for Parkinson's Disease

Authors: PAPAPETROPOULOS, SPIRIDON1; FFRENCH-MULLEN, JARLATH2;

Gene Expression, Volume 13, Number 3, 2006

Abstract:
Combining large-scale gene expression approaches and bioinformatics may provide insights into the molecular variability of biological processes underlying neurodegeneration.

To identify novel candidate genes and mechanisms, we conducted a multiregional gene expression analysis in postmortem brain. Gene arrays were performed utilizing Affymetrix HG U133 Plus 2.0 gene chips. Brain specimens from 21 different brain regions were taken from Parkinson's disease (PD) (n = 22) and normal aged (n = 23) brain donors. The rationale for conducting a multiregional survey of gene expression changes was based on the assumption that if a gene is changed in more than one brain region, it may be a higher probability candidate gene compared to genes that are changed in a single region.

Although no gene was significantly changed in all of the 21 brain regions surveyed, we identified 11 candidate genes whose pattern of expression was regulated in at least 18 out of 21 regions.

The expression of a gene encoding the mitochondria ribosomal protein S6 (MRPS6) had the highest combined mean fold change and topped the list of regulated genes.

The analysis revealed other genes related to apoptosis, cell signaling, and cell cycle that may be of importance to disease pathophysiology.

High throughput gene expression is an emerging technology for molecular target discovery in neurological and psychiatric disorders. The top gene reported here is the nuclear encoded MRPS6, a building block of the human mitoribosome of the oxidative phosphorylation system (OXPHOS). Impairments in mitochondrial OXPHOS have been linked to the pathogenesis of PD.

http://www.ingentaconnect.com/conten...00003/art00006

Current studies are of postmortem brains.

"For Parkinson’s disease, the search for novel drug targets has involved the use of microarray technologies in combination with a technique termed ‘voxelation’, which provides a quasi-three-dimensional approach to studying changes in gene expression in animal models of Parkinson’s disease"

Target validation
full article:

http://www.sciencedirect.com/science...9ec5142ae0f0c1

Using cDNA microarray to assess Parkinson's disease models and the effects of neuroprotective drugs

Silvia Mandel, Orly Weinreb and Moussa B. H. YoudimE-mail The Corresponding Author

Abstract

The remarkable progress made by molecular biology and molecular genetics during the past decade, and the advent of the novel tools of genomics and proteomics, are expected to reveal differential expression profiles of thousands of genes and proteins involved in the degeneration of dopamine-containing cells in Parkinson's disease and allow more focused treatments according to individual genotypes. Of particular interest is the application of microarrays in drug discovery and design to identify ‘fingerprints’ as potential candidate targets for drug intervention. The major microarray findings relevant to Parkinson's disease and its neurotoxin-induced animal and cell models will be discussed, with particular reference to the neuroprotective therapeutic potential that could arise from the development of drugs ‘a la carte’.


full article:
http://www.sciencedirect.com/science...efed4daf6c9c88

Outstanding issues with microarray study of PD: strengths and weaknesses

Gene-expression microarray technology is contributing to several important developments that have emerged over the past four years, and is rapidly becoming a technique that is employed routinely in neuroscience [25 and 46]. Nonetheless, it is a measurement procedure, with a set of general limitations that must be considered when employing human, animal or cell tissue. The reproducibility among independent replicates will depend on several factors, including animals of the same genetic background, animal weight and housing conditions, and the precise dissection of the brain region affected. Well-designed studies, with regard to neurotoxin type, dose and administration regimen, and brain region targeted (specifically SNc) are needed. The most precise gene-array results will be obtained by employing isolated nigral DA-containing neurons. Methods such as single-cell PCR [47] and laser capture microdissection [48] will be of significant importance to assess differential expression in the different cell types constituting the tissue in question. If working with cell lines special attention should be given to the passage number because biochemical pathways might be impaired.

Human brain tissue deserves even more rigorous attention because of the innate problems to obtain suitable autopsy materials, with careful consideration of the disease pathology, autopsy time, age and gender of patient, drug treatment and cause of death. The diversity of cell types in the brain and the variation between different subclasses of neurons necessitates cautious follow-up. Due to the inherent complexity of nervous tissue and the need to use post-mortem material, few microarray studies on neurodegenerative diseases, including PD, have been conducted. Moreover, in contrast to cDNA microarray studies with cancer, where many biological measures of the disease reveal several orders of magnitude differences in gene expression, alterations of genes in neurological diseases appear to be much more limited and often do not exceed a twofold change [49]. Furthermore, because alterations in gene expression less than twofold are not easily detected, significant biological changes might be missed. Thus, well-designed studies need to include sufficient numbers of samples and proper statistical methods and computational analyses. Typically, confirmatory studies that employ in situ or immunohistochemistry analyses should clarify the issue. Finally, the use of clustering analysis to detect particular functional group changes in gene expression might provide a higher level of examination of gene alterations. This is particularly useful for studies that examine complex changes in gene expression over time in neurodegenerative diseases, or in cases where sample availability is limited. Because an unprecedented amount of information is produced from microarray studies, bioinformatics and modeling expertise are increasingly crucial components.




A Correlation Analysis on Multiplex Three-Dimensional Brain Gene Expression Mapping of Parkinson Disease
http://ieeexplore.ieee.org/xpls/abs_...number=4382015

Future Neurology
January 2007, Vol. 2, No. 1, Pages 29-38
(doi:10.2217/14796708.2.1.29)

Gene-expression profiling in Parkinson’s disease: discovery of valid biomarkers, molecular targets and biochemical pathways
Spiridon Papapetropoulos* & Donald McCorquodale

In the past decade, several gene mutations have been described in families with a Mendelian inheritance pattern of Parkinson’s disease (PD), using linkage mapping. These cases represent only a small percentage (<5%) of the patients who develop PD. The current understanding of the mechanisms that underlie aspects of the neurodegenerative process of PD is based mainly on research of functional pathways related to these genes. However, even with knowledge of these pathways, the number of relevant genes may still be very large. In the post-genomic era, seven high-throughput gene array studies have attempted to identify candidate genes and biochemical pathways in PD. In this review, results from these studies and different factors influencing optimal target and biomarker discovery with gene-expression profiling are discussed.

http://www.futuremedicine.com/doi/ab...4796708.2.1.29

Thanks so much for the rundown on the current and intense efforts to use gene-knockouts, etc to identify what is going wrong, in what combinations of genetic expression, and the use of new technologies to asses current disease models for PD.

A lot of talent is working hard in this "decade (and counting) of the brain" to use techniques and knowledge only recently acquired to understand the mechanisms and miscues involved in producing what we call "PD." Some may look at the daunting nature of this task and say "not in my lifetime."

But I try to remind myself that what we knew about "PD" when MJF testified before Arlen Specter's Labor-HHS subcommittee in 1999 pales in comparison with what we know now. Is is going to be a longer road than we thought back then? Yes. But had we not poured so much money into PD and other neurological research over the last decade it would simply have taken us that much longer to get to what know today. In other words, we have to overcome whatever obstacles we encounter, whether we knew they existed or not. That's why we do research. So I say let's get on with it.

If the "several variants" hypothesis is correct, it would further explain why the big successes are found in small open label Phase I trials, and almost
always fail in subsequent larger phase II and III trials. It's because the odds
of picking at random five or six people (as in the Spheramine open label Phase I trial) most or all of whom happen to have the same or a close disease variant, and therefore would tend to respond similarly to a particular therapeutic application, are long but not impossible. So occasionally we have what appear to be "winners" in Phase I. But those "winners" will almost always lose when the patient population increases to 40 or 70 people, because of the odds against randomly picking a 70 patient cohort most or all of whom have the same or closely matching disease variants increases enormously.

For those who are still on a dopamine agonist, let me use a gambling analogy (sorry, I couldn't resist - I'll offer my 25 cents on that issue some other time). Hitting three sevens at a slot machine is hard but it can be done. Hitting three sevens 10 times in a row must be a near statistical impossibility. And so researchers occasionally get lucky in the Phase I "pick
six" portion, but face almost certain failure in the larger trial phases. And
that is worse than a real waste of time and money, because it's so misleading clinically.

Greg

Greg

Whhile attending a Clinical Research Learning Institute this qweekend sponsored by t he PDF, there was well-known statistician, Mike McDermott, Univ. of Rochester, who gave a very informative presentation. He made is simple, yet left me confused. lol

I asked about the subgroups, and he said if the sampling was large enough it would take care of such discrepancies. And of course he discussed how the endpoints should be carefully designed. Guess what example he used most often ? The Amgen study. He never would say it was a poor design, but it didn't take a rocket scientist to figure out that's why it was selected as a sample.

My question now is - OK - we've figured out an important piece of the puzzle - now what?

Where do we go from herhe in bringing about change??
Peg

Not every country uses double-blind clinical trials to test the pharmaceuticals used. I seem to recall that the PRC was using spheramine treatments - what were their results? What about other countries? This might be purely a "local" problem of relying on a broken model (double blind clinical trials) all the while bolstering its reputation by proclaiming it to be a "gold standard".

Also, what happened to grounded research where the theory was grounded in the data rather than the data being derived against a specific theoretical background? Did grounded research get lost in the dust?