Structure-based drug design approaches for predicting binding affinities of HIV1 protease inhibitors

Patient-specific modelling in drug design, development and selection including its role in clinical decision-making

Genomics has made enormous progress in the twelve years since the publication of the first draft human genome sequence, but it has not yet been translated into the clinic. Despite spiralling development costs, the number of new drug registrations is not increasing. One reason for this lies in the genetic complexity of disease. Most diseases involve dysregulation in pathways that involve many genes, and many (including most cancers) are themselves genetically heterogeneous. Systems biology involves the multi-level simulation of physiology, cell biology and biochemistry using complex computational techniques. We show here using case studies in cancer and HIV how such computational models, and particularly models based on individual patient data, can be used for drug design and development, and in the selection of the appropriate treatment for a given patient in the face of resistance mutations. If these techniques are to be adopted in routine clinical practice, clinicians will need better training in modern approaches to the integrated analysis of large-scale heterogeneous data and multi-scale models, while developers will need to provide much more usable tools. Investment in computational infrastructure is needed so that results can be returned on clinically relevant timescales and data warehouses designed with data protection as well as accessibility in mind.

GS-8374, a novel HIV protease inhibitor, does not alter glucose homeostasis in cultured adipocytes or in a healthy-rodent model system

Adverse effects induced by HIV protease inhibitors (PIs) are a significant factor in limiting their clinical success. PIs directly contribute to peripheral insulin resistance and alterations in lipid metabolism. GS-8374 is a novel PI with potent antiretroviral activity and a favorable resistance profile. Here we report on the potential of GS-8374 to adversely affect glucose and lipid homeostasis. Acute effects of GS-8374 and control PIs on glucose uptake and lipid accumulation were assessed in vitro in mouse OP9 and primary human adipocytes, respectively. GS-8374 and atazanavir showed no effect on insulin-stimulated deoxyglucose uptake, whereas ritonavir and lopinavir caused significant reductions. Similarly, in vitro lipid accumulation was not significantly affected in adipocytes treated with either GS-8374 or atazanavir. In euglycemic-hyperinsulinemic clamp experiments performed in rats during acute infusion of therapeutic levels of PIs, sustained serum GS-8374 levels of 8 μM had no effect on peripheral glucose disposal (similar to the findings for atazanavir). Comparable serum levels of lopinavir and ritonavir produced acute 19% and 53% reductions in in vivo glucose disposal, respectively. In conclusion, similar to atazanavir, but unlike ritonavir and lopinavir, GS-8374 neither affects insulin-stimulated glucose uptake in adipocytes in culture nor acutely alters peripheral glucose disposal in a rodent model system. These results dissociate the antiretroviral activity of GS-8374 from adverse effects on insulin sensitivity observed with some of the first-generation PIs and provide further support for the use of these experimental systems in the preclinical evaluation of novel PIs.

Computer-aided drug discovery and development

Computer-aided approaches have been widely used in pharmaceutical research to improve the efficiency of the drug discovery and development pipeline. To identify and design small molecules as clinically effective therapeutics, various computational methods have been evaluated as promising strategies, depending on the purpose and systems of interest. Both ligand and structure-based drug design approaches are powerful technologies, which can be applied to virtual screening for lead identification and optimization. Here, we review the progress in this field and summarize the application of some new technologies we developed. These state-of-the-art tools have been used for the discovery and development of active agents for various diseases, in particular for cancer therapies. The described protocols are appropriate for all drug discovery stages, but expertise is still needed to perform the studies based on the targets of interest.