Patent Document ID: 10114931
Application ID: 14327529
Patent Flag: 1

Claim One:
1. A method of preparing a herbal composition comprising multiple components for drug formulations, the method comprises the steps of: a) preparing multiple samples having different random concentrations of a herbal mixture that comprises multiple components, each of said sample comprises the same multiple components, said samples further comprise multiple doses of the mixture, wherein the identities of or interactions among the components are not known a priori; b) obtaining a dataset comprising pharmacodynamic parameters describing the dose-response of said samples of mixture; c) performing linear modeling on said dataset to identify the components having strong correlation with the response as potential active components; said modeling system comprises r ≈ r _ + ∑ i ⁢ w i ⁡ ( d i - d _ i ) + ∑ i ⁢ w i ′ ⁡ ( d i - d _ i ) 2 + ∑ i , j ⁢ w i , j ⁡ ( d i - d _ i ) ⁢ ( d j - d _ j ) ⁢ min w i , w i ′ , w i , j ⁢ { r - [ r _ + ∑ i ⁢ w i ( d i - d _ i ) + ∑ i ⁢ w i ′ ⁡ ( d i - d _ i ) 2 + ∑ i , j ⁢ w i , j ⁡ ( d i - d _ i ) ⁢ ( d j - d _ j ) ] } 2 wherein r is linearized response, r is average linearized response; w i is weight of the i th component, w i ′ is weight of the non-linear self-interaction behavior of the i th component, d i is the dose of the i th component, d i and d j are average doses of the i th and j th component, and w i,j is the weight of the interacting pair, min w i , w i ′ , w i , j is a minimization procedure to minimize the difference between r and said first modeling system by varying w i , w i ′, w i,j , wherein said first modeling system generates outputs comprising (i) optimal weights α i and β i,j , which are optimal values of w i and w i ′ and w i,j obtained from said minimization procedure, and (ii) total dose of the components D, which is defined as ∑ i = 1 n ⁢ ⁢ d i ; d) Using Bootstrapping approach to repeat steps b)-c) to refine the optimal weights α i and β i,j and potential active components; e) using in vitro assays or in silico methods to obtain parameters describing the rate of elimination of said potential active components from (d) and their active metabolites in a plurality of mammalian tissue systems; f) using in vitro assays or in silico methods to obtain parameters describing distribution of said potential active components from (d) and their active metabolites in a plurality of mammalian tissue systems; g) estimating the effect-time relationship of the potential active components by incorporating said parameters of (e) and (f); h) performing Subset-selection principal component analysis to infer the final set of active components based on parameters from d), e), f) and g); i) inputting the optimal weights, α i and β i,j , from (d) of final set of active components from h) into a second modeling system that comprises A = α 0 + ∑ i = 1 n ⁢ α i ⁢ x i + ∑ i = 1 n ⁢ ∑ j = 1 n ⁢ β i , j ⁢ x i ⁢ x j ⁢ ⁢ max x ⁢ ⁢ α 0 + ∑ i = 1 n ⁢ α i ⁢ x i + ∑ i = 1 n ⁢ ∑ j = 1 n ⁢ β i , j ⁢ x i ⁢ x j ⁢ s. t. ⁢ ⁢ ∑ i = 1 n ⁢ x i = 1 ⁢ x i ≥ 0 , ∀ i wherein, A is estimated response generated by said active components per total dose, α 0 is an average linearized response per total dose, x i and x j are defined as d′ i /D and d′ j /D respectively, which are fractions of said total dose D contributed by the i th and j th component respectively, and wherein D = ∑ i = 1 n ⁢ d i ′ , ⁢ max x is a maximization procedure achieved by varying x i , ∀ i is for all i values, wherein said second modeling system generates outputs comprising dosages of active components that generate maximal response; and j) using dosages of active components that generate the maximal response from (i) to prepare a herbal composition comprising multiple components, wherein the dosages for said multiple components are obtained from said dosages of active components that generate the maximal response.