A meta-analytical approach for estimating methane suppression from dietary additives in ruminants

Introduction Methane production inhibitors included in feed additives are increasingly used to suppress methane production in the rumen. Most products interact with the rumen microbiome and/or component biochemical pathways that scavenge free hydrogen ions (H+) to make methane (CH4 ). The capacity of these agents to inhibit rumen methane production is determined by the ability of the chemical to block methane production pathways, the amount of agent delivered to the rumen (i.e. the dose) and the absorption, distribution, metabolism and excretion (i.e. the pharmacokinetic) characteristics of the chemical that contribute to removal from the site of action (the rumen). The intrinsic inhibitory capacity of an agent determines the maximum rate of methane suppression. This maximal rate may reduce according to pharmacokinetic effects arising from dose rate and frequency. Most studies of additive methane reduction efficacy use total mixed ration (TMR; with the additive included into the ration) feeding systems and estimate methane reduction (absolute or relative) across a 24-hour period. Few studies report critical pharmacokinetic parameters, making it difficult to extrapolate findings into non-TMR systems (such as grazing) where differing doses and dose frequencies apply. Methods We consider the likely behaviour of a rumen-acting oral additive to reduce methane production applying basic pharmacokinetic principles to propose an analytical approach to data from multiple field studies employing different dose rates and dose frequencies to estimate methane suppression responses. This is based upon a logistic transformation of relative efficacy (percentage reduction in methane comparing treatment with control groups) as the dependent variable and includes total dose, dose frequency, quadratic and interaction terms between total dose and dose frequency as independent variables that potentially capture any pharmacokinetic effects on performance. The model was tested using simulation and verified against real data (cattle 3-nitrooxypropanol (3-NOP) methane-reduction studies). Results Good fit between predicted and observed methane suppression was obtained. Discussion We propose use of the general form of this logistic model with dose and dose frequency components included that address basic pharmacological impacts on methane suppression as the standard approach to meta-analysis.