Per a paper Collinearity: a review of methods to deal with it and a simulation study evaluating their performance, to quote basically the results:
Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold‐based pre‐selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the ‘folk lore’‐thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre‐analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.
where here GLM is defined, per an educational source, as for example:
The term general linear model (GLM) usually refers to conventional linear regression models for a continuous response variable given continuous and/or categorical predictors. It includes multiple linear regression, as well as ANOVA and ANCOVA (with fixed effects only).
In your particular case of testing for drug efficacy, my recommendation (which in total agreement per comments above) is to employ your knowledge/understanding of the underlying biochemistry in the pre‐analysis variable selection process. Further, selecting the least sensitive statistical approach in cases with collinearity is likely good practice.