Pharmazie
Refine
Document Type
- Conference Proceeding (2) (remove)
Language
- English (2) (remove)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Institute
- Pharmazie (2)
- Georg-Speyer-Haus (1)
- Medizin (1)
Objective: Establishment of an immunocompetent mouse model representing the typical progressive stages observed in malignant human gliomas for the in vivo evaluation of novel target-specific regimens.
Methods: Isolated clones from tumours that arose spontaneously in GFAP-v-src transgenic mice were used to develop a transplantable brain tumour model in syngeneic B6C3F1 mice. STAT3 protein was knocked down by infection of tumour cells with replication-defective lentivirus encoding STAT3-siRNA. Apoptosis is designed to be induced by soluble recombinant TRAIL + chemical Bcl-2/Bcl-xL inhibitors.
Results: Striatal implantation of 105 mouse tumour cells resulted in the robust development of microscopically (2 – 3 mm) infiltrating malignant gliomas. Immunohistochemically, the gliomas displayed the astroglial marker GFAP and the oncogenic form of STAT3 (Tyr-705-phosphorylated) which is found in many malignancies including gliomas. Phosphorylated STAT3 was particularly prominent in the nucleus but was also found at the plasma membrane of peripherally infiltrating glioma cells. To evaluate the role of STAT3 in tumour progression, we stably expressed siRNA against STAT3 in several murine glioma cell lines. The effect of STAT3 depletion on proliferation, invasion and survival will be first assessed in vitro and subsequently after transplantation in vivo. Upstream and downstream components of the STAT3 signalling pathway as well as possible non-specific side effects of STAT3-siRNA expression after lentiviral infection will be examined, too.
Conclusions: Its high rate of engraftment, its similarity to the malignant glioma of origin, and its rapid locally invasive growth should make this murine model useful in testing novel therapies for malignant gliomas.
Over the past two decades the “one drug – one target – one disease” concept became the prevalent paradigm in drug discovery. The main idea of this approach is the identification of a single protein target whose inhibition leads to a successful treatment of the examined disease. The predominant assumption is that highly selective ligands would avoid unwanted side effects caused by binding to secondary non-therapeutic targets. In recent years the results of post-genomic and network biology showed that proteins rarely act in isolated systems but rather as a part of a highly connected network [1]. In addition this connectivity leads to more robust systems that cannot be interfered by the inhibition of a single target of that network and consequently might not lead to the desired therapeutic effect [2]. Furthermore studies prove that robust systems are rather affected by weak inhibitions of several parts than by a complete inhibition of a single selected element of that system [3]. Therefore there is an increasing interest in developing drugs that take effect on multiple targets simultaneously but is concurrently a great challenge for medicinal chemists. There has to be a sufficient activity on each target as well as an adequate pharmacokinetic profile [4]. Early design strategies tried to link the pharmacophors of known inhibitors, however these methods often lead to high molecular weight and low ligand efficacy. We present a new rational approach based on a retrosynthetic combinatorial analysis procedure [5] on approved ligands of multiple targets. These RECAP fragments are used to design a large combinatorial library containing molecules featuring chemical properties of each ligand class. The molecules are further validated by machine learning models, like random forests and self-organizing maps, regarding their activity on the targets of interest.