Unlike traditional batch-based pharmaceutical manufacturing, where the active pharmaceutical ingredient (API) and the final drug product are often produced in different facilities at different locations, novel continuous pharmaceutical manufacturing strategies enable the production of both the API and the final drug product in the same integrated facility. The capacities of such integrated continuous facilities must be determined for potential products in the face of clinical trials uncertainty. Given a portfolio consisting of potential products in the development stage, the goal of capacity planning is to ensure the availability of enough production capacity to meet the projected demands of products, which vary from the launch to the peak-demand periods. To address this problem, we propose a multiscenario, multiperiod, mixed-integer linear programming (MILP) formulation that takes into account uncertainty in the outcome of clinical trials. We illustrate the proposed framework using several examples. The exponential increase in problem size with the number of products motivates us to develop an efficient solution method, which is discussed in Part 2 of this paper.