A　key　step　in　pharmacogenomic　studies　is　the　development　of　accurate　prediction　models　for　drug　response　based　on　individuals＇　genomic　information.　Recent　interest　has　centered　on　semiparametric　models　based　on　kernel　machine　regression,　which　can　flexibly　model　the　complex　relationships　between　gene　expression　and　drug　response.　However,　performance　suffers　if　irrelevant　covariates　are　unknowingly　included　when　training　the　model.　We　propose　a　new　semiparametric　regression　procedure,　based　on　a　novel　penalized　garrotized　kernel　machine　(PGKM),　which　can　better　adapt　to　the　presence　of　irrelevant　covariates　while　still　allowing　for　a　complex　nonlinear　model　and　gene-gene　interactions.　We　study　the　performance　of　our　approach　in　simulations　and　in　a　pharmacogenomic　study　of　the　renal　carcinoma　drug　temsirolimus.　Our　method　predicts　plasma　concentration　of　temsirolimus　as　well　as　standard　kernel　machine　regression　when　no　irrelevant　covariates　are　included　in　training,　but　has　much　higher　prediction　accuracy　when　the　truly　important　covariates　are　not　known　in　advance.