Combination therapies are used in the clinic to achieve cure, better efficacy and to circumvent resistant disease in patients. Initial assessment of the effect of such combinations, usually of two agents, is frequently performed using in vitro assays. In this review, we give a short summary of the types of analyses that were presented during the Preclinical and Early-phase Clinical Pharmacology Course of the Pharmacology and Molecular Mechanisms Group, European Organization for Research and Treatment on Cancer, that can be used to determine the efficacy of drug combinations. The effect of a combination treatment can be calculated using mathematical equations based on either the Loewe additivity or Bliss independence model, or a combination of both, such as Chou and Talalay's mediandrug effect model. Interactions can be additive, synergistic (more than additive), or antagonistic (less than additive). Software packages CalcuSyn (also available as CompuSyn) and Combenefit are designed to calculate the extent of the combined effects. Interestingly, the application of machinelearning methods in the prediction of combination treatments, which can include pharmacogenomic, genetic, metabolomic and proteomic profiles, might contribute to further refinement of combination regimens. However, more research is needed to apply appropriate rules of machine learning methods to ensure correct predictive models.Even as early as the 1960s, the majority of clinical treatments consisted of combination regimens. Combinations such as mechlorethamine, vincristine, procarbazine and prednisone (MOPP), and cyclophosphamide, hydroxydaunorubicin and oncovin with prednisone (CHOP) represented a breakthrough in the cure of lymphoma, while other combinations led to a high curation rate in childhood leukaemia (1). Depending on the type of combination used, the treatment rationale is to i) increase the efficacy of each separate drug without increasing toxicity, ii) add a drug which offers protection against toxicity, iii) bypass resistance development, or iv) target different subpopulations in a heterogeneous tumour. The initial clinical rationale was to achieve a better therapeutic effect (e.g. a complete response) than accomplished by each drug separately (e.g. only a partial response) (2). Historically, the selection of drugs to apply in combination therapies was based on the observation that each of the drugs showed antitumor activity against a certain tumour type, preferably with different toxicities of the two drugs. Doses and schedules were determined by trial and error. Soon thereafter, a complementary scientific approach was used to select combinations based on the mechanisms of action of each drug (3). An excellent example is the gemcitabine-cisplatin combination, which was initially developed by our group (4) (with the aim of preventing repair of DNA-platinum adducts) and is now standard therapy for tumours such as non-small cell lung cancer and bladder cancer. Another combination is 3303