E2. Combinatorial Drug Development - Biology

E2. Combinatorial Drug Development - Biology

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Instead of searching various organisms to find a possible drug from all the extractable molecules of the organism, people are now synthesizing an enormous number of randomly constructed molecules, and then selecting active molecules from this random pool by binding or other bioassays. Those peptides or oligonucleotides that bind flurorescent-labeled protein can be determined, as shown below.

Figure: Combinatorial Drug Development.

These structures can then be decorated or used to design peptide or oligonucleotide mimetics, which would be more resistant to enzymatic degradation. Another way to make a large library of peptide inhibitors is to make them using genetic engineering.

Recently, peptides made of D-amino acids have been synthesized. These are much more resistant to proteases than normal L-amino acid peptides. D amino acid peptides can only be made in the lab, and not through genetic engineering. An intriguing variation of this technique has recently been developed. Investigators have synthesized entire proteins in the lab using D-amino acids. Combinatorial L-peptide libraries were made using genetic engineering in bacteria. L-peptides were selected that bound with high affinity to the D-protein. The corresponding D-peptides were made in the lab, which then were found to bind to the normal L-protein.

In another combinatorial technique, single stranded RNA molecules called aptamers can be synthesized and tested for the binding/inhibitory activity for a enzyme. RNA molecules, which can form complex secondary and tertiary structure, can also present, given the appropriate sequence, a complementary binding surface to sites on proteins. Aptamers with high affinity are sequenced. Bases that appear to be necessary for high affinity binding are identified by comparing the sequence of different aptamers. This knowledge is then used to synthesize even tighter binding aptamers, in an process that mimics evolutionary selection for high affinity binding. Such a high affinity aptamer was recently made to bind to and inhibit Factor IXa, an active enzyme required for initiation and propagation of blood clotting. Traditional anticoagulant drugs are extremely valuable in treating and preventing inappropriate clot formation, which can lead to strokes (brain attacks) and heart attacks. The problem with these drugs is that they must not tip the clotting/anticlotting balance to far in the direction of clot prevention, since there are many times when clot formation is the appropriate biological response (such as in the prevention of hemorrhagic stoke). Rusconi et al. realized that once they had developed the high affinity aptamer for factor IXa binding, they immediately could synthesize an anedote for the aptamer. It would have a complementary sequence to the Factor IXa binding bases of the drug aptamer, that would allow it to bind to the original apatmer and form a double-stranded RNA sequence. The crystal structure of the homodimer transcription factor, NF-κB (p50)2 bound to an RNA aptamer has been determined. The RNA sequence is dissimilar to the DNA sequence which binds the transcription factor, even though both have similar dissociation constants.

Jmol: Updated Nf-Kb(P50)2 Complexed To A High- Affinity RNA Aptamer Jmol14 (Java) | JSMol (HTML5)

Disruption of redox homeostasis for combinatorial drug efficacy in K-Ras tumors as revealed by metabolic connectivity profiling

Background: Rewiring of metabolism induced by oncogenic K-Ras in cancer cells involves both glucose and glutamine utilization sustaining enhanced, unrestricted growth. The development of effective anti-cancer treatments targeting metabolism may be facilitated by the identification and rational combinatorial targeting of metabolic pathways.

Methods: We performed mass spectrometric metabolomics analysis in vitro and in vivo experiments to evaluate the efficacy of drugs and identify metabolic connectivity.

Results: We show that K-Ras-mutant lung and colon cancer cells exhibit a distinct metabolic rewiring, the latter being more dependent on respiration. Combined treatment with the glutaminase inhibitor CB-839 and the PI3K/aldolase inhibitor NVP-BKM120 more consistently reduces cell growth of tumor xenografts. Maximal growth inhibition correlates with the disruption of redox homeostasis, involving loss of reduced glutathione regeneration, redox cofactors, and a decreased connectivity among metabolites primarily involved in nucleic acid metabolism.

Conclusions: Our findings open the way to develop metabolic connectivity profiling as a tool for a selective strategy of combined drug repositioning in precision oncology.

Keywords: Combinatorial drug treatment Glutamine Glycolysis Metabolic cancer therapy Metabolic connectivity Metabolic rewiring Metabolic signature Precision oncology.

Discoveries about the molecular basis of disease provide unprecedented opportunities to find new medicines. Discovery and formulation of new drug takes more than 14 years due to different barriers in clinical trials. Side effect of a drug on a particular disease could be studied and used as an initiative to repurpose the drug against disease with different therapeutics in combinations.

CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models


Cellular and Molecular Bioengineering, Vol. 4, No. 1, March 2011 (© 2010) pp. 28–45
DOI: 10.1007/s12195-010-0143-x

A grand challenge of computational systems biology is to create a molecular pathway model of the whole cell. Current approaches involve merging smaller molecular pathway models’ source codes to create a large monolithic model (computer program) that runs on a single computer. Such a larger model is difficult, if not impossible, to maintain given ongoing updates to the source codes of the smaller models. This paper describes a new system called CytoSolve that dynamically integrates computations of smaller models that can run in parallel across different machines without the need to merge the source codes of the individual models. This approach is demonstrated on the classic Epidermal Growth Factor Receptor (EGFR) model of Kholodenko. The EGFR model is split into four smaller models and each smaller model is distributed on a different machine. Results from four smaller models are dynamically integrated to generate identical results to the monolithic EGFR model running on a single machine. The overhead for parallel and dynamic computation is approximately twice that of a monolithic model running on a single machine. The CytoSolve approach provides a scalable method since smaller models may reside on any computer worldwide, where the source code of each model can be independently maintained and updated.