## Overview

- MuSyC, originally published in

*Meyer CT, Wooten DJ, Paudel BB, et al. Cell Systems. 2019*and updated in

*Wooten DJ, Meyer CT, et al., under review, 2020*, is a framework for calculating drug synergy which distinguishes between different types of synergistic interactions. Synergistic efficacy (beta) measures the changes in maximal effect over single agents due to the combination. Synergistic potency (alpha) measures the change in potency of one drug given the presence of the other drug. Importantly these types of synergy align with common clinical motives for treating diseases with drug combinations: improve outcomes by escalating effect (synergistic efficacy) and reduce off-target toxicity by minimizing doses (synergistic potency).

## FAQ

See the Help page. Use is subject to Terms and
Conditions.

Currently, the MuSyC portal can only handle the two drug case. For collaborative inquiries of this nature, please
email musyc@gmail.com.

Contrary to prior frameworks, MuSyC quantifies the synergy of a drug combination. Once the best combination from
the screen is selected, users should look for a minimum dose that achieves the desired effect magnitude. In
other words, dose optimization IS ALWAYS done based on the observed effect. MuSyC helps in identifying
combinations for which the desired effect is achievable by the combination but not the single drugs (synergistic
efficacy) or where the doses required to achieve that effect are lowered due to the drugs interacting
(synergistic potency).

The MuSyC fitting algorithm currently handles such cases by assuming the binary condition to satisfy
[drug2]->inf. In this case, the MuSyC equation reduces to a Hill equation with an EC50 defined by C1/alpha1. See
Section 6 of Supplement in (Wooten DJ et al. 2020) for proof of this condition.

Use a unique identifier in the optional "batch" column of the upload. Each batch will be self-contained and not
sampled from for fitting dose-response surfaces from other batches.

We have found the MuSyC framework to be fairly robust in a wide range of sample density and designs. See Figure
S4 in Wooten et al. 2020 for complete analysis. However, the exact sampling design requirements are
idiosyncratic to the noise profile of a particular assay; therefore, no universal standard exists. Typically,
the Matrix (also called Checkerboard) sampling strategy is most robust at the cost of higher data density
demands. For extremely limited sampling where the full dose-response profile of each single agent cannot be
captured, we recommend using Highest Single Agent (HSA) at the max concentration of both compounds as HSA
approximates synergistic efficacy in this condition. Subsequent screens can identify synergistically potent
combinations from the hits by increasing the sampling.

The current fit algorithm leveraged in the MuSyC portal is described in the Methods section of Wooten et al.
2020. We use a Monte Carlo algorithm as suggested by
Motulsky and Christopoulos
(Chapter 17, pg 104) for
estimating asymmetric 95% confidence intervals of each parameter. Briefly, this is done by fitting all the data
using standard non-linear least squares regression (TFR option in
SciPy's curve_fit).
Based on
this optimal fit, noise is added to every data point proportional to the root mean square error of the optimal
fit. The new "noise-added" data is then fit again to generate a new parameter set. This process is run 100 times
and the 95% confidence intervals for all parameters are calculated from the ensemble.

According to Motulsky and Christopoulos,
data should not be smoothed before fitting because this can arbitrarily
reduce the noise dispersion in non-linear ways resulting in noise-profiles that are not homoskedastic, a common
assumption of most non-linear least-square optimizers.

## Funding

CTM was supported by National Science Foundation (NSF) Graduate Student Fellowship Program (GRFP) [Award #1445197]; CFL and DJW were supported by the National Science Foundation [MCB 1411482 and MCB 1715826, respectively]; CFL and VQ were supported by the National Institutes of Health (NIH) [U54-CA217450 and U01-CA215845]; VQ was supported by NIH [R01-186193].