UK guidelines for the sustainable control of parasites in sheep (SCOPS) aim to delay further development of anthelmintic drug resistance. This study describes a computer model evaluation of resistance development with a novel oral formulation of derquantel-abamectin, to inform recommendations for use. Two different farm management scenarios, based on UK field data, were modelled to simulate low refugia (non SCOPS) or high refugia (SCOPS) worm populations. The effect on resistance allele frequencies and field efficacy of several treatment scenarios using the novel active derquantel (DQL), a spiroindole (SI), as either a single or multiple active formulation with abamectin (ABA), a macrocyclic lactone (ML), under the two farm management systems was evaluated. The initial resistance allele frequency for DQL was set at 0.0001, assuming that resistance in the UK is low, and for ML at 0.165 or 0.8, assuming that resistant nematode populations exist in the UK. DQL resistance reached a level at which a reduction in field efficacy might be detected (resistance allele frequency 0.25) by year 16 when used sequentially, and by year 31 when used in annual rotation (ABA) with SCOPS management inputs, and by year 5 (sequential) and by year 10 (annual rotation) with non SCOPS management inputs. ML resistance reached a level at which a reduction in ABA field efficacy might be detected (resistance allele frequency 0.25) by year 4 when used sequentially, and by year 8 when used in annual rotation with DQL and SCOPS management inputs, and by year 1 (sequential) and by year 2 (annual rotation) with non SCOPS management inputs. No detectable reduction in field efficacy was observed for DQL-ABA after 40 years of use with SCOPS management inputs for simulations using an initial ML resistance allele frequency of 0.165 and 0.8. A detectable reduction in field efficacy was observed for DQL-ABA by year 32 (initial ML resistance allele frequency=0.165) and by year 6 (initial ML resistance allele frequency=0.8) with non SCOPS management inputs. In summary, the results suggest that formulating DQL in combination with ABA confers a substantial advantage in delaying the development of both DQL and ML resistance, and the provision of adequate refugia further extends this advantage.